{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"CNN1.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"svppQZlrES-H","colab_type":"text"},"source":["# CNN1 SETTINGS\n","\n","SUBJECT'S DATA SELECTION:                                                                                                  \n","- **A**: Signal samples: 7794, EEG channels: 64, Testset letters: 100                                                           \n","- **B**: Signal samples: 7794, EEG channels: 64, Testset letters: 100 "]},{"cell_type":"code","metadata":{"id":"9nw7AE3rLWlk","colab_type":"code","outputId":"7803c2e1-5c85-4905-b669-015a77b7721f","executionInfo":{"status":"ok","timestamp":1576424435959,"user_tz":-60,"elapsed":37713,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":333}},"source":["import matplotlib.pyplot as plt\n","import seaborn as sns\n","import pandas as pd\n","import numpy as np     \n","import random           \n","from scipy.io import loadmat\n","from scipy import signal\n","from google.colab import drive\n","import warnings\n","import string\n","import os\n","\n","from keras.layers import *\n","from keras.models import Model\n","from keras import backend as K\n","from keras import Sequential\n","from keras.callbacks import ModelCheckpoint\n","from keras.callbacks import EarlyStopping\n","from sklearn.metrics import confusion_matrix\n","from sklearn.preprocessing import scale\n","from sklearn.preprocessing import MinMaxScaler\n","\n","# Install mne library for topoplot\n","!pip install mne\n","import mne\n","\n","warnings.filterwarnings('ignore')\n","drive.mount('/content/drive')\n","\n","####################### SETTINGS ########################\n","# Set this variable with the desidered subject's letter #\n","SUBJECT_SELECTED = \"B\"                                  #\n","#########################################################\n","\n","subject_names = [\"A\", \"B\"]\n","\n","# Check for errors in the settings\n","if SUBJECT_SELECTED not in subject_names:\n","    raise ValueError(\"SUBJECT_SELECTED value {} is invalid.\\nPlease enter one of the following parameters {}\".format(SUBJECT_SELECTED, subject_names))\n","\n","# Google drive data paths\n","MODEL_LOCATIONS_FILE_PATH = 'drive/My Drive/AY1920_DT_P300_SPELLER_03/Trained_models/CNN1/' + SUBJECT_SELECTED\n","SUBJECT_TRAIN_FILE_PATH = 'drive/My Drive/AY1920_DT_P300_SPELLER_03/Dataset/Subject_' + SUBJECT_SELECTED + '_Train.mat'\n","SUBJECT_TEST_FILE_PATH = 'drive/My Drive/AY1920_DT_P300_SPELLER_03/Dataset/Subject_' + SUBJECT_SELECTED + '_Test.mat'\n","CHANNEL_LOCATIONS_FILE_PATH = 'drive/My Drive/AY1920_DT_P300_SPELLER_03/Dataset/channels.csv'\n","CHANNEL_COORD = 'drive/My Drive/AY1920_DT_P300_SPELLER_03/Dataset/coordinates.csv'\n","\n","# Channel selection\n","CHANNELS = [i for i in range(64)]"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Using TensorFlow backend.\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["<p style=\"color: red;\">\n","The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n","We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n","or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n","<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Collecting mne\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a1/7c/ad1b52a3fdd4be8f55e183f1eff7d76f48cd1bee83c5630f9c26770e032e/mne-0.19.2-py3-none-any.whl (6.4MB)\n","\u001b[K     |████████████████████████████████| 6.4MB 5.5MB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.6/dist-packages (from mne) (1.17.4)\n","Requirement already satisfied: scipy>=0.17.1 in /usr/local/lib/python3.6/dist-packages (from mne) (1.3.3)\n","Installing collected packages: mne\n","Successfully installed mne-0.19.2\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/numba/decorators.py:146: RuntimeWarning: Caching is not available when the 'parallel' target is in use. Caching is now being disabled to allow execution to continue.\n","  warnings.warn(msg, RuntimeWarning)\n"],"name":"stderr"},{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"CVrJuHCMFCSl","colab_type":"text"},"source":["# Training set processing\n","\n","1.   Application of bandpass filter **(0.1-20Hz)**;\n","2.   Down-sampling signals from 240Hz to 120Hz;\n","3.   Obtain windows of 650ms at the start of every flashing (175ms)\n","4.   Normalization of samples in each window: **Zi = (Xi - mu) / sigma**;\n","5.   Reshape each window to be a 3D tensor with dimensions: **(N_SAMPLES, 78, 64)**;\n","6.   Obtain **(noP300 / P300)** class ratio to balance out dataset during training;"]},{"cell_type":"code","metadata":{"id":"78aU0w6-Lp0y","colab_type":"code","outputId":"d1b22ce0-1858-4007-85a8-e169316ae6ef","executionInfo":{"status":"ok","timestamp":1576424445216,"user_tz":-60,"elapsed":3901,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":134}},"source":["if not os.path.exists(SUBJECT_TRAIN_FILE_PATH):\n","    print(\"Missing file: {}\".format(SUBJECT_TRAIN_FILE_PATH))\n","else:\n","    # Load the required data\n","    data = loadmat(SUBJECT_TRAIN_FILE_PATH)\n","    # Get the variables of interest from the loaded dictionary\n","    signals = data['Signal']\n","    flashing = data['Flashing']\n","    stimulus = data['StimulusType']\n","    word = data['TargetChar']\n","    \n","    SAMPLING_FREQUENCY = 240\n","    # From the dataset description we know that there are 15 repetitions\n","    REPETITIONS = 15\n","    # Compute the duration of the recording in minutes\n","    RECORDING_DURATION = (len(signals))*(len(signals[0]))/(SAMPLING_FREQUENCY*60)\n","    # Compute number of trials\n","    TRIALS = len(word[0])\n","    # Set flag to True to balance the training set\n","    BALANCE_DATASET = False\n","\n","    print(\"**********************************\")\n","    print(\"       TRAIN SET INFORMATION      \")\n","    print(\"**********************************\")\n","    print(\"Sampling Frequency: %d Hz [%.2f ms]\" % (SAMPLING_FREQUENCY, (1000/SAMPLING_FREQUENCY)))\n","    print(\"Session duration:   %.2f min\" % RECORDING_DURATION)\n","    print(\"Number of letters:  %d\" % TRIALS)\n","    print(\"Spelled word:       %s\" % ''.join(word))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["**********************************\n","       TRAIN SET INFORMATION      \n","**********************************\n","Sampling Frequency: 240 Hz [4.17 ms]\n","Session duration:   46.01 min\n","Number of letters:  85\n","Spelled word:       VGREAAH8TVRHBYN_UGCOLO4EUERDOOHCIFOMDNU6LQCPKEIREKOYRQIDJXPBKOJDWZEUEWWFOEBHXTQTTZUMO\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"IXuVC7puKcLt","colab_type":"code","colab":{}},"source":["# Application of butterworth filter\n","b, a = signal.butter(4, [0.1/SAMPLING_FREQUENCY, 20/SAMPLING_FREQUENCY], 'bandpass')\n","for trial in range(TRIALS):\n","    signals[trial, :, :] = signal.filtfilt(b, a, signals[trial, :, :], axis=0)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"jS1FuKDuixnF","colab_type":"code","outputId":"f4fc2a89-1f8c-48a9-f060-727e77b8aa94","executionInfo":{"status":"ok","timestamp":1576424450421,"user_tz":-60,"elapsed":2945,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":50}},"source":["# Down-sampling of the signals from 240Hz to 120Hz\n","DOWNSAMPLING_FREQUENCY = 120\n","SCALE_FACTOR = round(SAMPLING_FREQUENCY / DOWNSAMPLING_FREQUENCY)\n","SAMPLING_FREQUENCY = DOWNSAMPLING_FREQUENCY\n","\n","print(\"# Samples of EEG signals before downsampling: {}\".format(len(signals[0])))\n","\n","signals = signals[:, 0:-1:SCALE_FACTOR, :]\n","flashing = flashing[:, 0:-1:SCALE_FACTOR]\n","stimulus = stimulus[:, 0:-1:SCALE_FACTOR]\n","\n","print(\"# Samples of EEG signals after downsampling: {}\".format(len(signals[0])))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["# Samples of EEG signals before downsampling: 7794\n","# Samples of EEG signals after downsampling: 3897\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"bGaRKwu9MIvP","colab_type":"code","outputId":"12e9ec3d-fac7-46c8-d21c-64e302f8399e","executionInfo":{"status":"ok","timestamp":1576424452130,"user_tz":-60,"elapsed":2050,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":33}},"source":["# Number of EEG channels\n","N_CHANNELS = len(CHANNELS)\n","# Window duration after each flashing [ms]\n","WINDOW_DURATION = 650\n","# Number of samples of each window\n","WINDOW_SAMPLES = round(SAMPLING_FREQUENCY * (WINDOW_DURATION / 1000))\n","# Number of samples for each character in trials\n","SAMPLES_PER_TRIAL = len(signals[0])\n","\n","train_features = []\n","train_labels = []\n","\n","count_positive = 0\n","count_negative = 0\n","\n","for trial in range(TRIALS):\n","    for sample in (range(SAMPLES_PER_TRIAL)):\n","        if (sample == 0) or (flashing[trial, sample-1] == 0 and flashing[trial, sample] == 1):\n","            lower_sample = sample\n","            upper_sample = sample + WINDOW_SAMPLES\n","            window = signals[trial, lower_sample:upper_sample, :]                \n","            # Features extraction\n","            train_features.append(window)\n","            # Labels extraction\n","            if stimulus[trial, sample] == 1:\n","                count_positive += 1\n","                train_labels.append(1) # Class P300\n","            else:\n","                count_negative += 1\n","                train_labels.append(0) # Class no-P300\n","\n","# Get negative-positive classes ratio\n","train_ratio = count_negative/count_positive\n","\n","# Convert lists to numpy arrays\n","train_features = np.array(train_features)\n","train_labels = np.array(train_labels)\n","\n","# 3D Tensor shape (SAMPLES, 64, 78)\n","dim_train = train_features.shape\n","print(\"Features tensor shape: {}\".format(dim_train))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Features tensor shape: (15300, 78, 64)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"kKMlV1Mqapew","colab_type":"code","colab":{}},"source":["# Data normalization Zi = (Xi - mu) / sigma\n","for pattern in range(len(train_features)):\n","    train_features[pattern] = scale(train_features[pattern], axis=0)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"xTVrfKzJH2aE","colab_type":"text"},"source":["# Testing set processing\n","\n","1.   Application of bandpass filter **(0.1-20Hz)**;\n","2.   Down-sampling signas from 240Hz to 120Hz;\n","3.   Obtain windows of 650ms at the start of every flashing (175ms)\n","4.   Normalization of samples in each window: **Zi = (Xi - mu) / sigma**;\n","5.   Reshape each window to be a 3D tensor with dimensions: **(N_SAMPLES, 78, 64)**;\n","6.   Calculate weights vector to balance samples importance and obtain correct accuracy estimation;"]},{"cell_type":"code","metadata":{"id":"Nm-bZZsKTliz","colab_type":"code","outputId":"06dcbd08-67c2-4826-de3d-99dc9b2a24d6","executionInfo":{"status":"ok","timestamp":1576424466616,"user_tz":-60,"elapsed":11382,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":134}},"source":["# Test data loading\n","if not os.path.exists(SUBJECT_TEST_FILE_PATH):\n","    print(\"Missing file: {}\", SUBJECT_TEST_FILE_PATH)\n","else:\n","    # Load the required data\n","    data_test = loadmat(SUBJECT_TEST_FILE_PATH)\n","    # Get the variables of interest from the loaded dictionary\n","    signals_test = data_test['Signal']\n","    flashing_test = data_test['Flashing']\n","    word_test =  data_test['TargetChar']\n","    stimulus_code_test = data_test['StimulusCode']\n","\n","    SAMPLING_FREQUENCY = 240\n","    # From the dataset description we know that there are 15 repetitions\n","    REPETITIONS = 15\n","    # Compute the duration of the recording in minutes\n","    RECORDING_DURATION_TEST = (len(signals_test))*(len(signals_test[0]))/(SAMPLING_FREQUENCY*60)\n","    # Compute number of trials\n","    TRIALS_TEST = len(word_test[0])\n","    # Number of samples for each character in trials\n","    SAMPLES_PER_TRIAL_TEST = len(signals_test[0])\n","    \n","    print(\"**********************************\")\n","    print(\"        TEST SET INFORMATION      \")\n","    print(\"**********************************\")\n","    print(\"Sampling Frequency: %d Hz [%.2f ms]\" % (SAMPLING_FREQUENCY, (1000/SAMPLING_FREQUENCY)))\n","    print(\"Session duration:   %.2f min\" % RECORDING_DURATION_TEST)\n","    print(\"Number of letters:  %d\" % TRIALS_TEST)\n","    print(\"Spelled word:       %s\" % ''.join(word_test))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["**********************************\n","        TEST SET INFORMATION      \n","**********************************\n","Sampling Frequency: 240 Hz [4.17 ms]\n","Session duration:   54.12 min\n","Number of letters:  100\n","Spelled word:       MERMIROOMUHJPXJOHUVLEORZP3GLOO7AUFDKEFTWEOOALZOP9ROCGZET1Y19EWX65QUYU7NAK_4YCJDVDNGQXODBEV2B5EFDIDNR\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"SvCresQJm25A","colab_type":"code","colab":{}},"source":["# Create characters matrix\n","char_matrix = [[0 for j in range(6)] for i in range(6)]\n","s = string.ascii_uppercase + '1' + '2' + '3' + '4' + '5' + '6' + '7' + '8' + '9' + '_'\n","\n","# Append cols and rows in a list\n","list_matrix = []\n","for i in range(6):\n","    col = [s[j] for j in range(i, 36, 6)]\n","    list_matrix.append(col)\n","for i in range(6):\n","    row = [s[j] for j in range(i * 6, i * 6 + 6)]\n","    list_matrix.append(row)\n","\n","# Create StimulusType array for the test set (missing from the given database)\n","stimulus_test = [[0 for j in range(SAMPLES_PER_TRIAL_TEST)] for i in range(TRIALS_TEST)]\n","stimulus_test = np.array(stimulus_test)\n","\n","for trial in range(TRIALS_TEST):\n","    counter=0\n","    for sample in range(SAMPLES_PER_TRIAL_TEST):\n","        index = int(stimulus_code_test[trial, sample]) - 1\n","        if not index == -1:\n","            if word_test[0][trial] in list_matrix[index]:\n","                stimulus_test[trial, sample] = 1\n","            else:\n","                stimulus_test[trial, sample] = 0"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"UNy5oI8ooMAw","colab_type":"code","colab":{}},"source":["# Application of butterworth filter\n","b, a = signal.butter(4, [0.1/SAMPLING_FREQUENCY, 20/SAMPLING_FREQUENCY], 'bandpass')\n","for trial in range(TRIALS_TEST):\n","    signals_test[trial, :, :] = signal.filtfilt(b, a, signals_test[trial, :, :], axis=0)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"8CpKeth5qOjr","colab_type":"code","outputId":"d68e24c4-ce36-4404-855d-ae1352a5c898","executionInfo":{"status":"ok","timestamp":1576424470296,"user_tz":-60,"elapsed":8124,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":50}},"source":["# Down-sampling of the signals from 240Hz to 120Hz\n","DOWNSAMPLING_FREQUENCY = 120\n","SCALE_FACTOR = round(SAMPLING_FREQUENCY / DOWNSAMPLING_FREQUENCY)\n","SAMPLING_FREQUENCY = DOWNSAMPLING_FREQUENCY\n","\n","print(\"# Samples of EEG signals before downsampling: {}\".format(len(signals_test[0])))\n","\n","signals_test = signals_test[:, 0:-1:SCALE_FACTOR, :]\n","flashing_test = flashing_test[:, 0:-1:SCALE_FACTOR]\n","stimulus_test = stimulus_test[:, 0:-1:SCALE_FACTOR]\n","\n","print(\"# Samples of EEG signals after downsampling: {}\".format(len(signals_test[0])))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["# Samples of EEG signals before downsampling: 7794\n","# Samples of EEG signals after downsampling: 3897\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"48sta55OVXFy","colab_type":"code","outputId":"fa6aa9d1-0f93-418b-ff8b-5fd0fa9cab82","executionInfo":{"status":"ok","timestamp":1576424471791,"user_tz":-60,"elapsed":7564,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":33}},"source":["# Number of EEG channels\n","N_CHANNELS = len(CHANNELS)\n","# Window duration after each flashing [ms]\n","WINDOW_DURATION = 650\n","# Number of samples of each window\n","WINDOW_SAMPLES = round(SAMPLING_FREQUENCY * (WINDOW_DURATION / 1000))\n","# Number of samples for each character in trials\n","SAMPLES_PER_TRIAL_TEST = len(signals[0])\n","\n","test_features = []\n","test_labels = []\n","windowed_stimulus = []\n","\n","count_positive = 0\n","count_negative = 0\n","\n","for trial in range(TRIALS_TEST):\n","    for sample in (range(SAMPLES_PER_TRIAL_TEST)):\n","        if (sample == 0) or (flashing_test[trial, sample-1] == 0 and flashing_test[trial, sample] == 1):\n","            lower_sample = sample\n","            upper_sample = sample + WINDOW_SAMPLES\n","            window = signals_test[trial, lower_sample:upper_sample, :]\n","            # Extracting number of row/col in a window\n","            number_stimulus = int(stimulus_code_test[trial, sample])\n","            windowed_stimulus.append(number_stimulus)\n","            # Features extraction\n","            test_features.append(window)\n","            # Labels extraction\n","            if stimulus_test[trial, sample] == 1:\n","                count_positive += 1\n","                test_labels.append(1) # Class P300\n","            else:\n","                count_negative += 1\n","                test_labels.append(0) # Class no-P300\n","\n","# Get test weights to take into account the number of classes \n","test_weights = []\n","for i in range(len(test_labels)):\n","    if test_labels[i] == 1:\n","        test_weights.append(len(test_labels)/count_positive)\n","    else:\n","        test_weights.append(len(test_labels)/count_negative)\n","test_weights = np.array(test_weights)\n","\n","# Convert lists to numpy arrays\n","test_features = np.array(test_features)\n","test_labels = np.array(test_labels)\n","\n","# 3D tensor (SAMPLES, 64, 78)\n","dim_test = test_features.shape\n","print(\"Features tensor shape: {}\".format(dim_test))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Features tensor shape: (18000, 78, 64)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"-VY8Nr04YI9F","colab_type":"code","colab":{}},"source":["# Data normalization Zi = (Xi - mu) / sigma\n","for pattern in range(len(test_features)):\n","    test_features[pattern] = scale(test_features[pattern], axis=0)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"-VK4MFXTjHoM","colab_type":"text"},"source":["# CNN1 model definition, training and testing\n","\n","1.   Function definiton for randomization of weights and biases;\n","2.   **Scaled_tanh(x)** activation function definition;\n","3.   ANN model definition (2 Conv1D layers, 2 dense layers);\n","4.   Training of the network over weighted dataset;\n","5.   CNN1 performance assessment;"]},{"cell_type":"code","metadata":{"id":"eTIc_w_TcOmK","colab_type":"code","colab":{}},"source":["# Randomizing function for bias and weights of the network\n","def cecotti_normal(shape, dtype = None, partition_info = None):\n","    if len(shape) == 1:\n","        fan_in = shape[0]\n","    elif len(shape) == 2:\n","        fan_in = shape[0]\n","    else:\n","        receptive_field_size = 1\n","        for dim in shape[:-2]:\n","            receptive_field_size *= dim\n","        fan_in = shape[-2] * receptive_field_size\n","    return K.random_normal(shape, mean = 0.0, stddev = (1.0 / fan_in))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"VeyyvqGgcTsa","colab_type":"code","colab":{}},"source":["# Custom tanh activation function\n","def scaled_tanh(z):\n","    return 1.7159 * K.tanh((2.0 / 3.0) * z)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"BuMCOgJ3ST-h","colab_type":"code","colab":{}},"source":["# Build the model\n","def CNN1_model(channels=64, filters=10):\n","    model = Sequential([\n","        Conv1D(\n","            filters = filters,\n","            kernel_size = 1,\n","            padding = \"same\",\n","            bias_initializer = cecotti_normal,\n","            kernel_initializer = cecotti_normal,\n","            use_bias = True,\n","            activation = scaled_tanh,\n","            input_shape = (78, channels)\n","        ),\n","        Conv1D(\n","            filters = 50,\n","            kernel_size = 13,\n","            padding = \"valid\",\n","            strides = 11,\n","            bias_initializer = cecotti_normal,\n","            kernel_initializer = cecotti_normal,\n","            use_bias = True,\n","            activation = scaled_tanh,\n","        ),\n","        Flatten(),\n","        Dense(100, activation=\"sigmoid\"),\n","        Dense(1, activation=\"sigmoid\")\n","    ])\n","    model.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics = ['accuracy'])\n","    return model"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"jVMdFs4quaZL","colab_type":"code","outputId":"101cc1d0-a9f7-4430-bc3b-a0c4c3884283","executionInfo":{"status":"ok","timestamp":1576424483408,"user_tz":-60,"elapsed":8234,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":505}},"source":["# Training parameters\n","BATCH_SIZE = 256\n","EPOCHS = 200\n","VALID_SPLIT = 0.05\n","SHUFFLE = 1 # set to 1 to shuffle subsets during training\n","\n","# Model summary\n","CNN1_model(channels=64, filters=10).summary()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4409: The name tf.random_normal is deprecated. Please use tf.random.normal instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n","\n","Model: \"sequential_1\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","conv1d_1 (Conv1D)            (None, 78, 10)            650       \n","_________________________________________________________________\n","conv1d_2 (Conv1D)            (None, 6, 50)             6550      \n","_________________________________________________________________\n","flatten_1 (Flatten)          (None, 300)               0         \n","_________________________________________________________________\n","dense_1 (Dense)              (None, 100)               30100     \n","_________________________________________________________________\n","dense_2 (Dense)              (None, 1)                 101       \n","=================================================================\n","Total params: 37,401\n","Trainable params: 37,401\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"GEPpgyxyTl-u","colab_type":"code","outputId":"295e6ca1-c6fd-4c0b-b073-acfdbd5d72cb","executionInfo":{"status":"ok","timestamp":1576419828720,"user_tz":-60,"elapsed":88347,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# Model definition\n","model = CNN1_model(channels=64, filters=10)\n","\n","# Callback to save best model only\n","checkpoint = ModelCheckpoint(filepath=MODEL_LOCATIONS_FILE_PATH + \"/\" + \"model\" + \".h5\", \n","                             monitor='val_loss', \n","                             mode='min',\n","                             save_best_only=True)\n","\n","# Callback to stop when loss on validation set doesn't decrease in 50 epochs\n","earlystop = EarlyStopping(monitor = 'val_loss', \n","                              mode = 'min', \n","                              patience = 50, \n","                              restore_best_weights = True)\n","\n","# Callback to keep track of model statistics\n","history = model.fit(x=train_features, \n","                    y=train_labels, \n","                    batch_size=BATCH_SIZE, \n","                    epochs=EPOCHS, \n","                    validation_split=VALID_SPLIT, \n","                    callbacks=[checkpoint, earlystop],\n","                    shuffle=SHUFFLE,\n","                    class_weight={0: 1., 1: train_ratio})"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n","\n","Train on 14535 samples, validate on 765 samples\n","Epoch 1/200\n","14535/14535 [==============================] - 5s 353us/step - loss: 0.3781 - acc: 0.6131 - val_loss: 0.3148 - val_acc: 0.7229\n","Epoch 2/200\n","14535/14535 [==============================] - 0s 33us/step - loss: 0.3035 - acc: 0.7389 - val_loss: 0.2608 - val_acc: 0.7229\n","Epoch 3/200\n","14535/14535 [==============================] - 1s 36us/step - loss: 0.2820 - acc: 0.7604 - val_loss: 0.2435 - val_acc: 0.7948\n","Epoch 4/200\n","14535/14535 [==============================] - 0s 33us/step - loss: 0.2795 - acc: 0.7666 - val_loss: 0.2371 - val_acc: 0.7660\n","Epoch 5/200\n","14535/14535 [==============================] - 0s 33us/step - loss: 0.2742 - acc: 0.7631 - val_loss: 0.2330 - val_acc: 0.8013\n","Epoch 6/200\n","14535/14535 [==============================] - 1s 36us/step - loss: 0.2670 - acc: 0.7773 - val_loss: 0.2272 - val_acc: 0.8065\n","Epoch 7/200\n","14535/14535 [==============================] - 1s 35us/step - loss: 0.2661 - acc: 0.7745 - val_loss: 0.2262 - val_acc: 0.7987\n","Epoch 8/200\n","14535/14535 [==============================] - 1s 36us/step - loss: 0.2649 - acc: 0.7768 - val_loss: 0.2251 - val_acc: 0.7961\n","Epoch 9/200\n","14535/14535 [==============================] - 1s 35us/step - loss: 0.2609 - acc: 0.7783 - val_loss: 0.2201 - val_acc: 0.7882\n","Epoch 10/200\n","14535/14535 [==============================] - 0s 34us/step - loss: 0.2656 - acc: 0.7760 - val_loss: 0.2260 - val_acc: 0.7922\n","Epoch 11/200\n","14535/14535 [==============================] - 1s 38us/step - loss: 0.2590 - acc: 0.7822 - val_loss: 0.2180 - val_acc: 0.7882\n","Epoch 12/200\n","14535/14535 [==============================] - 1s 34us/step - loss: 0.2578 - acc: 0.7836 - val_loss: 0.2273 - val_acc: 0.7686\n","Epoch 13/200\n","14535/14535 [==============================] - 1s 38us/step - loss: 0.2540 - acc: 0.7839 - val_loss: 0.2202 - val_acc: 0.8013\n","Epoch 14/200\n","14535/14535 [==============================] - 1s 36us/step - loss: 0.2541 - acc: 0.7842 - val_loss: 0.2306 - val_acc: 0.7856\n","Epoch 15/200\n","14535/14535 [==============================] - 1s 37us/step - loss: 0.2541 - acc: 0.7851 - val_loss: 0.2189 - val_acc: 0.7974\n","Epoch 16/200\n","14535/14535 [==============================] - 1s 37us/step - loss: 0.2513 - acc: 0.7855 - val_loss: 0.2120 - val_acc: 0.8222\n","Epoch 17/200\n","14535/14535 [==============================] - 1s 38us/step - loss: 0.2481 - acc: 0.7867 - val_loss: 0.2175 - val_acc: 0.8013\n","Epoch 18/200\n","14535/14535 [==============================] - 1s 37us/step - loss: 0.2481 - acc: 0.7897 - val_loss: 0.2193 - val_acc: 0.7752\n","Epoch 19/200\n","14535/14535 [==============================] - 1s 38us/step - loss: 0.2493 - acc: 0.7867 - val_loss: 0.2171 - val_acc: 0.7699\n","Epoch 20/200\n","14535/14535 [==============================] - 1s 37us/step - loss: 0.2468 - acc: 0.7847 - val_loss: 0.2128 - val_acc: 0.8144\n","Epoch 21/200\n","14535/14535 [==============================] - 1s 37us/step - loss: 0.2446 - acc: 0.7909 - val_loss: 0.2086 - val_acc: 0.7804\n","Epoch 22/200\n","14535/14535 [==============================] - 1s 37us/step - loss: 0.2450 - acc: 0.7933 - val_loss: 0.2215 - val_acc: 0.7791\n","Epoch 23/200\n","14535/14535 [==============================] - 1s 38us/step - loss: 0.2420 - acc: 0.7914 - val_loss: 0.2072 - val_acc: 0.8196\n","Epoch 24/200\n","14535/14535 [==============================] - 1s 36us/step - loss: 0.2396 - acc: 0.7963 - val_loss: 0.2125 - val_acc: 0.8039\n","Epoch 25/200\n","14535/14535 [==============================] - 0s 34us/step - loss: 0.2354 - acc: 0.7990 - val_loss: 0.2013 - val_acc: 0.8196\n","Epoch 26/200\n","14535/14535 [==============================] - 1s 37us/step - loss: 0.2344 - acc: 0.8020 - val_loss: 0.2298 - val_acc: 0.7529\n","Epoch 27/200\n","14535/14535 [==============================] - 1s 38us/step - loss: 0.2344 - acc: 0.7972 - val_loss: 0.2092 - val_acc: 0.8000\n","Epoch 28/200\n","14535/14535 [==============================] - 1s 39us/step - loss: 0.2402 - acc: 0.7957 - val_loss: 0.2196 - val_acc: 0.7621\n","Epoch 29/200\n","14535/14535 [==============================] - 1s 36us/step - loss: 0.2315 - acc: 0.8015 - val_loss: 0.2085 - val_acc: 0.8196\n","Epoch 30/200\n","14535/14535 [==============================] - 1s 36us/step - loss: 0.2326 - acc: 0.8034 - val_loss: 0.2138 - val_acc: 0.7765\n","Epoch 31/200\n","14535/14535 [==============================] - 1s 37us/step - loss: 0.2315 - acc: 0.8000 - val_loss: 0.2050 - val_acc: 0.8013\n","Epoch 32/200\n","14535/14535 [==============================] - 1s 36us/step - loss: 0.2261 - acc: 0.8091 - val_loss: 0.2123 - val_acc: 0.7765\n","Epoch 33/200\n","14535/14535 [==============================] - 1s 38us/step - loss: 0.2264 - acc: 0.8083 - val_loss: 0.2110 - val_acc: 0.7765\n","Epoch 34/200\n","14535/14535 [==============================] - 1s 36us/step - loss: 0.2241 - acc: 0.8081 - val_loss: 0.2077 - val_acc: 0.7922\n","Epoch 35/200\n","14535/14535 [==============================] - 1s 37us/step - loss: 0.2252 - acc: 0.8084 - val_loss: 0.2115 - val_acc: 0.8118\n","Epoch 36/200\n","14535/14535 [==============================] - 1s 37us/step - loss: 0.2209 - acc: 0.8104 - val_loss: 0.2049 - val_acc: 0.8052\n","Epoch 37/200\n","14535/14535 [==============================] - 1s 36us/step - loss: 0.2197 - acc: 0.8146 - val_loss: 0.2060 - val_acc: 0.8105\n","Epoch 38/200\n","14535/14535 [==============================] - 1s 37us/step - loss: 0.2162 - acc: 0.8189 - val_loss: 0.2054 - val_acc: 0.8052\n","Epoch 39/200\n","14535/14535 [==============================] - 1s 37us/step - loss: 0.2138 - acc: 0.8211 - val_loss: 0.2106 - val_acc: 0.7974\n","Epoch 40/200\n","14535/14535 [==============================] - 1s 38us/step - loss: 0.2135 - acc: 0.8193 - val_loss: 0.2045 - val_acc: 0.8353\n","Epoch 41/200\n","14535/14535 [==============================] - 1s 39us/step - loss: 0.2106 - acc: 0.8181 - val_loss: 0.2058 - val_acc: 0.8157\n","Epoch 42/200\n","14535/14535 [==============================] - 1s 36us/step - loss: 0.2071 - acc: 0.8299 - val_loss: 0.2088 - val_acc: 0.8275\n","Epoch 43/200\n","14535/14535 [==============================] - 1s 37us/step - loss: 0.2052 - acc: 0.8276 - val_loss: 0.2107 - val_acc: 0.8105\n","Epoch 44/200\n","14535/14535 [==============================] - 1s 35us/step - loss: 0.2022 - acc: 0.8288 - val_loss: 0.2232 - val_acc: 0.8118\n","Epoch 45/200\n","14535/14535 [==============================] - 1s 37us/step - loss: 0.2020 - acc: 0.8316 - val_loss: 0.2174 - val_acc: 0.7908\n","Epoch 46/200\n","14535/14535 [==============================] - 1s 36us/step - loss: 0.1985 - acc: 0.8332 - val_loss: 0.2105 - val_acc: 0.8327\n","Epoch 47/200\n","14535/14535 [==============================] - 1s 37us/step - loss: 0.1961 - acc: 0.8400 - val_loss: 0.2092 - val_acc: 0.8065\n","Epoch 48/200\n","14535/14535 [==============================] - 1s 36us/step - loss: 0.1954 - acc: 0.8370 - val_loss: 0.2081 - val_acc: 0.8444\n","Epoch 49/200\n","14535/14535 [==============================] - 1s 35us/step - loss: 0.1905 - acc: 0.8409 - val_loss: 0.2124 - val_acc: 0.8131\n","Epoch 50/200\n","14535/14535 [==============================] - 1s 38us/step - loss: 0.1877 - acc: 0.8460 - val_loss: 0.2180 - val_acc: 0.8013\n","Epoch 51/200\n","14535/14535 [==============================] - 1s 38us/step - loss: 0.1857 - acc: 0.8453 - val_loss: 0.2154 - val_acc: 0.8092\n","Epoch 52/200\n","14535/14535 [==============================] - 1s 38us/step - loss: 0.1807 - acc: 0.8543 - val_loss: 0.2167 - val_acc: 0.8131\n","Epoch 53/200\n","14535/14535 [==============================] - 1s 38us/step - loss: 0.1785 - acc: 0.8543 - val_loss: 0.2086 - val_acc: 0.8288\n","Epoch 54/200\n","14535/14535 [==============================] - 1s 36us/step - loss: 0.1731 - acc: 0.8592 - val_loss: 0.2146 - val_acc: 0.8118\n","Epoch 55/200\n","14535/14535 [==============================] - 1s 37us/step - loss: 0.1697 - acc: 0.8618 - val_loss: 0.2171 - val_acc: 0.8235\n","Epoch 56/200\n","14535/14535 [==============================] - 1s 37us/step - loss: 0.1681 - acc: 0.8643 - val_loss: 0.2186 - val_acc: 0.8105\n","Epoch 57/200\n","14535/14535 [==============================] - 1s 36us/step - loss: 0.1631 - acc: 0.8702 - val_loss: 0.2220 - val_acc: 0.8078\n","Epoch 58/200\n","14535/14535 [==============================] - 1s 37us/step - loss: 0.1586 - acc: 0.8711 - val_loss: 0.2225 - val_acc: 0.8170\n","Epoch 59/200\n","14535/14535 [==============================] - 1s 36us/step - loss: 0.1550 - acc: 0.8775 - val_loss: 0.2200 - val_acc: 0.8431\n","Epoch 60/200\n","14535/14535 [==============================] - 1s 37us/step - loss: 0.1503 - acc: 0.8847 - val_loss: 0.2198 - val_acc: 0.8144\n","Epoch 61/200\n","14535/14535 [==============================] - 1s 38us/step - loss: 0.1465 - acc: 0.8863 - val_loss: 0.2217 - val_acc: 0.8261\n","Epoch 62/200\n","14535/14535 [==============================] - 1s 35us/step - loss: 0.1441 - acc: 0.8882 - val_loss: 0.2266 - val_acc: 0.8000\n","Epoch 63/200\n","14535/14535 [==============================] - 1s 36us/step - loss: 0.1385 - acc: 0.8916 - val_loss: 0.2228 - val_acc: 0.8261\n","Epoch 64/200\n","14535/14535 [==============================] - 1s 38us/step - loss: 0.1344 - acc: 0.8942 - val_loss: 0.2245 - val_acc: 0.8275\n","Epoch 65/200\n","14535/14535 [==============================] - 1s 37us/step - loss: 0.1314 - acc: 0.8989 - val_loss: 0.2272 - val_acc: 0.8327\n","Epoch 66/200\n","14535/14535 [==============================] - 1s 37us/step - loss: 0.1267 - acc: 0.9047 - val_loss: 0.2298 - val_acc: 0.8248\n","Epoch 67/200\n","14535/14535 [==============================] - 1s 37us/step - loss: 0.1216 - acc: 0.9081 - val_loss: 0.2259 - val_acc: 0.8248\n","Epoch 68/200\n","14535/14535 [==============================] - 1s 38us/step - loss: 0.1174 - acc: 0.9123 - val_loss: 0.2380 - val_acc: 0.8248\n","Epoch 69/200\n","14535/14535 [==============================] - 1s 38us/step - loss: 0.1145 - acc: 0.9172 - val_loss: 0.2346 - val_acc: 0.8366\n","Epoch 70/200\n","14535/14535 [==============================] - 1s 37us/step - loss: 0.1104 - acc: 0.9192 - val_loss: 0.2386 - val_acc: 0.8405\n","Epoch 71/200\n","14535/14535 [==============================] - 1s 38us/step - loss: 0.1087 - acc: 0.9217 - val_loss: 0.2371 - val_acc: 0.8235\n","Epoch 72/200\n","14535/14535 [==============================] - 1s 38us/step - loss: 0.1056 - acc: 0.9212 - val_loss: 0.2414 - val_acc: 0.8301\n","Epoch 73/200\n","14535/14535 [==============================] - 1s 36us/step - loss: 0.1011 - acc: 0.9277 - val_loss: 0.2382 - val_acc: 0.8392\n","Epoch 74/200\n","14535/14535 [==============================] - 1s 38us/step - loss: 0.0961 - acc: 0.9311 - val_loss: 0.2420 - val_acc: 0.8209\n","Epoch 75/200\n","14535/14535 [==============================] - 1s 37us/step - loss: 0.0948 - acc: 0.9331 - val_loss: 0.2483 - val_acc: 0.8353\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"eJHaj2f1VFYZ","colab_type":"code","outputId":"be2c6164-20e5-49b0-85cb-8df2814b654c","executionInfo":{"status":"ok","timestamp":1576419829416,"user_tz":-60,"elapsed":89032,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":265}},"source":["plt.figure(figsize=(15,4))\n","plt.style.use('tableau-colorblind10')\n","\n","# Plots of loss curves during training\n","plt.subplot(1,2,1)\n","plt.plot(history.history['loss'], label=\"training loss\")\n","plt.plot(history.history['val_loss'], label=\"validation loss\")\n","plt.legend()\n","\n","# Plots of accuracy curves during training\n","plt.subplot(1,2,2)\n","plt.plot(history.history['acc'], label=\"training accuracy\")\n","plt.plot(history.history['val_acc'], label=\"validation accuracy\")\n","plt.legend()\n","\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA3AAAAD4CAYAAACt4QT/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOnQoAGvXrqV58+Y0b96coKAg4uPjb+o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ybpooRRYawHnBiY2ZT2u4OkgPnBBCCCGEsBgn\nLl5h9K9bWX7oNB3uqs53D7emYXXXsm6WuA0sM4Bz84a4OZCWAlY21HBx5PDZuLJulRBCCAujlOoJ\n/AuwBr7TWn+ca7k38CNQOX2d8VrrxUopH+AgEJa+6mat9VO3q91CiIrjbPw1Rv68iejLObPJws/G\noxRMHdySp9o1xMpKxrbdKSwzgKvsDToV4k5B5brUcHFg/dGzZd0qIYQQFkQpZQ1MBboBUcA2pdRC\nrfWBbKu9CczWWn+tlGoKLAZ80pcd1Vo3v51tFkJULNeSUxnw7Vp2Rl6iW+OaOQqQBHt58HYvf+p6\nOJdhC0VZsNwADsw4uPQA7sLVJFJS07CxtirbtgkhhLAUrYAjWutjAEqpmUA/IHsAp4GMnCY3IPq2\ntlAIUWFprXni181sPHae2f9ox6DgumXdJFFOWGY0U9nLPKYXMqnh6oDWcO5KUhk2SgghhIWpA0Rm\nex6V/lp2E4FhSqkoTO/bs9mW1VNK7VJKrVVKtS/VlgohKpyPlu/n520RvNsnUII3kUORAjilVE+l\nVJhS6ohSanwey59SSu1VSoUqpTakp4lkLHstfbswpVSPkmx8vlw9AQWx5nPVJ32W+X3Rsbfl8EII\nIUS6h4AftNaeQG9ghlLKCjgNeGutg4CXgF+VUnlWH1BKjVZKbVdKbT937txta7gQouzM3XWSNxbt\n5uEQH97s6V/WzRHlTKEplEXM8f9Va/1N+vp9gclAz/RAbijgB9QGViqlGmmtU0v4PHKysQeXWplz\nwXVsWANHW2sW7I2im2+tUj20EEKIO8YpwCvbc8/017IbBfQE0FpvUko5AFW11meBpPTXdyiljgKN\ngO25D6K1ngZMAwgJCdElfRJCiLIzL/Qkv26PoEole2q5OlLbzRF7G2vGzNpKG5+q/PeRNjLxtrhB\nUcbAFZrjr7XOXuKxEibnn/T1Zmqtk4DjSqkj6fvbVAJtL1i2ueCc7Gzo7luLBXui+PegELkQhBBC\nlIRtQEOlVD1M4DYUeDjXOieBrsAPSilfwAE4p5SqBlzUWqcqpeoDDYFjt6/pQohSt/2/YOcMgUNu\nWHQpIYlnf9/OL9siqO3mSEqa5my2Ka+83Z34Y3QHHGytb2eLRQVRlAAurxz/1rlXUkqNxaSB2AFd\nsm27Ode2uccHoJQaDYwG8Pb2Lkq7C1fZGyKzDt0/0JMFe6LYGXmRFt5VSuYYQggh7lha6xSl1DPA\nMswUAd9rrfcrpd4FtmutFwIvA98qpV7EfLk5QmutlVIdgHeVUslAGvCU1vpiGZ2KEKI0rP0E7F1u\nCOCWHohm1C+bORt/jYm9A3i9hz+21lYkp6ZxJi6R05cTaVzDFTdHuzJqeB4it8CSV2D4QnCQScLL\nWolVodRaTwWmKqUexpRNfqwY25Z8eoibF+yflzkX3H3+dbBSigV7oiSAE0IIUSK01osxxUmyv/Z2\ntp8PAG3z2G4uMLfUGyiEKMWi4fQAACAASURBVBvxp+FKTNY/5xqcjb/GW3/uZtr/jtC0phsLn+yY\n457U1toKL/dKeLlXKsOG52PXz3B6NxxbA037l3Vrbt3p3eBR3wTYFVBRipgUJcc/u5lAxm+2uNuW\nnMreJniLPw1AVWcH2jWoxh97om7L4YUQQgghhOXTWvPGwlDqvDGPF+fuYP/pWIjelbk8Lmwt4xfs\not6EP/hu41HGdfVlx6u9Kk6HgtZwZIX5+diaMm3KLYvcCj/eB9M6wOJxxd/+0J+ZQ7TKUlECuMwc\nf6WUHSbHf2H2FZRSDbM97QOEp/+8EBiqlLJPHyPQENh6680uguxzwaXrH+jJ3uhYjp2Pvy1NEEII\nIYQQlut6SiqPzdjEh8v3U9vNkanrDuP/wV/8d+5c0rDiurLn13m/8unKA/QP9OLgm/fx2YDgijW2\nLWaf6RCxcYDja8u6NTfn9G74dRB83w3OhYH33bBvDsQVY2rOXTNg1iPw5/Ol184iKjSA01qnABk5\n/geB2Rk5/ukVJwGeUUrtV0qFYsbBPZa+7X5gNqbgyVJgbKlXoMxQOX2+jGwBXL9ATwAWSC+cEEII\nIYTlSLs9t5fZxV9L5r5v1jBj63Hev68ZW1/pSdT7A5g0IJgGyUc4mFKDtUn16Fkpgn2v38cvI9rS\nqEaes4WUb+HLzWPrp+DisXLRA1Usaz8xPW6RW6HrRHguFPp/DToNtn1btH2c3AR/vgj2rnB0FVw4\nmv+6V89D4qUSaXp+ijQPnNZ6sda6kda6gdb6g/TX3k4foI3W+nmttZ/WurnWunN64Jax7Qfp2zXW\nWi8pndPIg1t6rZTYrPor9au6EFC7sgRwQgghhBCWIvYEfFTH3FjfJqcvJ9JxygpWHY5h+rA2vNHT\nH6UU1V0ceLmrLx2dT1OjUWua39MHn+QImlZOu21tK3FHVkCtZhAw2DyvSL1wp3bA2o/BbyA8vwfa\nvQh2lcC9HjS5D7Z/D9evFryP2JMwa5jJ7hu1EqxszHb5+fsdmNoKkhNL9lyyKVIAVyHZOIBzTXNR\nZ9Mv0JP1R89x/sq1fDYUQgghhBAVxolNkJIIW6eV2iG01hyOieOnLcd4euYW3p00kaiz51n0ZEdG\ntGmQc+X406grMVRt2IZqfl0Abao4VkSJF03b7+oO1ZtCpWq3J4C7eg5Cf4XU5JvfR2oyLHoOnGvA\nfVPAwS3n8rufgWuxsPvX/Pdx/QrMfMjs66GZUK0x+PaF0J/zDtAuHIHQX8D/AbB1vPm2F8JyAzgw\nkfLlnN28/QM9SdOav/YVI+dVCCGEEEKUT6dDzWP4cog/U2K7TU5NY8GeSB78bh1VX51D4/cW8diM\nTezeuZ6vbb5hR9dT9PK7YXYsiE5vT+3mUKcFWNnCif+VWLtuq6OrTKphw+6gFNTrCMfWmsImpeXQ\nX/BVG1gwBuaPvvn02P9NMeP3ev/zxuANwLMV1AmBzV+Zc8xNp8H8J+HsAXjwe6jayLweMsoEfvvn\n3bjN6vdNJ1L7l2+uzUVk+QFcrjzdYC8PPCs78ceeyHw2EkIIIYQQFcbpUDN9lE4tsDfl9YWhjJyx\nifm7I7malJLnOlprdkVe5IU526nzxjz6T1vH+qPnGNDMi+8ebs2+N/qw/qFaAHidzycoOx0Kygpq\nBphemDotzBiqiih8BTh6mHMAqN8Jrp6FcwdL/lhJcbBgLMx6GFxrwT3PmSBp4di8A6yCnD8M6z41\nUx406ZP3OkrB3WPNuL7DS3Mu02mwdLypOtn9A7jr3qxlddtCtSaw7buc25wOhf3zoc3TpqeyFJXY\nPHDlkpsXHPjDRO5WptqPUop+gZ5M33yUxOspONpZ9lsghBBCCGGx0lLh9B4IGgZn9pr5ytq+aG7O\ns1mwJ5KvVmwDazt+2HIMB1tr7m1ck56+tYi7lsyhmLjMf3HXkrGzseJ+/zqMaF2fHk1rY2udrc9j\nzwHzeHITXLt8Y+/O6VDTW2PnbJ7XvQc2fmHGWtmV0Bxvu2fCstfg6S3gXL1k9pmbTjPj3+66N/M+\nmnodzeOxtSalsiRcvwInNsJfL0NcFLR7GTqNB2s7sHOBNR+Atb1Jg8z1e8233YueA1sn6PVZwev6\n9jXxwqap0Li3eS05wfT8HVwEbcZC6zE5t1EKQh6HJePMGLuM4HbVe+DoDvc8W/z3oJgsO3rJmAvu\nyhlwzeri7hfgydR1h1kZdob7AzzLsIFCCCGEEOKmXQiH5KtQq7n5t2CMCazq3pO5yuXE67w2aw1h\nVT+gav0WrG01lQV7o1iwJ4o/95npietUdqRJDTcebVWPZnXceaC5Fx6V7PM+ZsxeExwkJ6RPbN0v\n5/LoXVC/c9bzum1hw2SI2mZ6sG5VarIJahIvml6gzq/f+j7zEr0LEi6Y9MkMlb1NAZDja6HNmBu3\nCV9uAhqnAua4O3sQ9v5uevFi9mfVq3CvByOXglfrrHU7vGLGN26YbFITe35ceBC3/XvzN9Dvq8KD\nWysbU11z+RvmfF3rwMyhcGon9Pg473MEaDYEVk4wx6rTwqTIHlkJ976bd7pmCbP8AA5MGmW2AK5j\nw+q4OdoyY+txevjWws6mAs3FIYQQQgghjOzjzSrXhSX/Z+bryhbAvbYwlHFpv1KDS3BsJV26n6fL\ngyFMeaAFEReuUtXZHhcH26IdT2szrqppfzNWK3x5zgAu/jRciTHBZAavVial8sTGkgng9v5u7m3d\nvGD7d6ayYmkUzAhfDiho0DXn6/U7wd45ppPEKlsocXgZ/DYY2jwDPT7If79/vmCC2aqNTPAT9ChU\n9zVBb+4eSqWgy9uQfA22fAUXjxaennhwkWljs4eLdp5Bj8Kaj01Aduk4XDkHQ37JP/USzHQCgYNh\n92/Q/T34+11TPLHVE0U75i2y8DFwN84FB2BnY83DIT78vusktV6fx9hZW9l8/Dy6NAdkCiGEEEKI\nknU6FGwc01MWK4H/QDN8JikOgA1HzxK2eRH/cNgILUaanrPNXwFmWE29qs5FD97AjP9KuGDK6t/V\nxaQYZr9/zB5QZrB3NePhTm681bM1KaMbJkONADOXWcIF2DMr73WT4mDOiJsffxe+HDxbgpNHztfr\ndYTr8aaXKvNY8fDXS+nbLct/nwkXIWqrKfLx9GZ4cLrpZWtyX/7ppUpBjw/hnufNJNwRGwr+V7Uh\n3PevoqVbgukxCx5uehWTr8GIvwoO3jKEjIKUazDvCYjcDB3/z/x93QaW3QPnlp4emceEg188GML9\n/nWYsfU4328+xlfrw2lYzYUP7m/GoOC6t7mhQgghhCgzEevNTdjjq8C1dlm3RhQgOTWNLRHnsbex\nJsjTHZvToSY4yugJChoOO3+EfXNJChzOs7+s4w+330hzr49Vj49MRcgd002vjkvN4jfgzF7zWMPP\nBGb758OZPSagg/SKmMq0KTvvtrDje0i9bsZ23ayDC0za6IM/QN125ribvzIBiMrVL7PyHdO+03tM\nsFSc4145C9E7ofObNy6r1wFQJuDxamVe+/tdiDsFAYNMD+HFY+BR/8Zts1e1LA6loNu75l9paPuC\nCY7vfjqrA6gwNQPAq41JnXT3MX97t4ll98DZOkKl6nkGcDbWVvTyq8OvI9sR8+EDfP9IG5ztbRj8\n/QbGzdtJSmoFnnBRCCGEEEW3+zeT+rZnZtm14XIUnN5d8DppKaYqYElkDF2JySq/X86diUtk+qaj\nDPrveqq+Oof2n6+g1WdLqfrqLBJP7mJHijcbj50j/lqyScmr5gu7ZvDh8v08fPU36qpzWPX9t7kv\nbPOUeR9zVxAsqpj95rGGX1ZlwvDlWctzFzDJUPce01sTvevmjgvm977+n1CloSm+oZRJVzwfBkf+\nzrnuyU0mvdL7HpN2uOU/xTvW0fT9Nex24zKnKiZ4ObbGPI/cAtu+hVZPQqfXzGtHVua93yPLTVXL\n2sHFa09pc64OvT4pevCWISNlsvObYF2MntxbZNkBHOQ5F1xuro62jLy7AZvH9WBsh0b8c9VBuk9d\nxdl4mexbCCGEsGhpqVklxPfMKt35rQqy/A34obcpTJGfLf+BXx+EU9tv/XirP4AZ/cvufIvg2Pl4\nHvxuHbVen8c/ftnMxmPnGBzszdzH2zNzZFteCrTBkWt8cdiJtpOX4zpuNnXenM9XV1rDqR1ErfmO\nl51WQfBj4NPO7NSjgUmP2/5fU4SkuM7uN3UVHD3MTX/tIJNGmSE6NGf6ZAbvu83jrcwHd3ipGX/X\n/uWsqpB+/cGlNmz+Mmu9lGumCqObNzzyOzTsYUrqXzlb9GOFLzcTYNcMzHt5vY4mFTLxEix81mS9\ndX3LvL8eDXIGtRnSUk1gl72qZUXn9wA8sQb8H7yth70zArg8euDyYmdjzZeDW/LDsLvZdPw8LT5Z\nwtaI86XcQCGEEEKUmahtZhxRvQ5w7pBJhysLp0NNOfUt0/JennLNlKIHuHTi1o934ai5+Y6LuvV9\nlbD4a8m8tmAXvu//yZID0bze3Y+dr/Yi6v0BfPtwGwY292ZICx/ebm6ypT5/5h/8MboDH/VtTrcm\ntVik2nFdW/NtpZ/QlardmHbXZqwJlHffRI/rmX2m9y1Dwx7pf0MXzSTiV87kLGCSoVJVqNr45sej\naQ3rPzM9RNmDBWs7aDXa9IbF7DOvrZtk5kG773PTE9jjQxOsripi+mFSnOmBu6vbjWmZGep3Mumg\ns4aZHsA+n2f1OjbsbtKScwfImVUtexTjxMs5pUwQX9TxdiXkzgjgLkcVawLAx9rUZ+NL3bGxVrSf\nsoJlB6JLsYFCCCGEKDNhi824qL5fmsf8CkIUVep1M99XcSTFwaUIM45ry9emIERuu342wQGUTNB1\nKcI8xhy49X2VkNS0NL7fdJSG7yzk4xUHGBpcl8Nv9+WDvs0J8vJA5b5JjjYFTDzqBtAv0Ivx3f34\n4dG7WfLKIGyb9sFKaazvmwwOlXNu5323uene/FXxJohOvW6ClerZA7juZh9HV2WlpNYOynv7uvfA\nyc2mJ6q4jq8xc461feHGVL0WI7KKs8Tsg/99DoFDs1I8q9xlSuXv+rnwFM60VJj7uPkbDn4s//W8\n7zbXy4kNEDA4Z6plw27mC4eIDTm3CV9uAsIGXYp61iIflh/AuXmZC+5KTLE2C/LyYPsrvWha042B\n361j47FzpdRAIYQQQpSZw0tMel3lutCohynAkJZy8/tbPA5+vK9422SMq+rwf3At1qT3ZZeaDP/7\nl6kIaO8Kl0/dfPsAUpJMwQkwKYHlwMpDp2nxyVJG/bKZ+lWd2TKuBz8Ov4c6lQuo6pe7gEk2qtu7\n0O9rU93whoXpY8cuhJsxhUV1/rD526jpn/Va7SBwqmoqL0bnU8AkQ922JljP6CkrqrQUWPcZuNSC\n5o/cuNzRHZoPgz2zYf6TJmDt8VHOdTr8nxm7tnR8wWmzf08059Lr06wCJXmxqwR17zb77PlxzmV1\n25mAMnca5ZF8qlqKYrP8AC6fqQSKooqzPUuf7kwdNyf6fL2GPaculXDjhBBCCFFmLhwxN+WNepnn\ngUNMmfiM4gw3I3Krqd5X0Fi23DIqGwYPN3NubfoyZ/rZ3tlmPH/7V8z4q7hbDOAuRwLpN/Fnb1MP\n3IUjsPOnG3q89kXH0vur1XT7chWXr13ntxFt+d9L3WnlU7Xg/aWlmqIveaUrgpkUunkB84A17Wfe\ny4yxY1fPmaqKW765MYDOkFnAJFsAp6xMT9eRlRC9I+8CJhnqtjXr57f/3HQa7JsLX7UxY+favQw2\n+UwunlGcJWYf9PzkxiDJwQ26vm3K3e+bm/c+Qn81abotnzAl8gvT/z8wauWNk3bb2JsUy/BlWcHi\nlbOm9++uPIqiiGK7AwK4bJN534Qaro6seLYLleyt6f7lKsLPxpVg44QQQghRZsKWmMfGPc1jwx7m\nRvdm0yhTr5teHYCoHUXf7swecxPsXBPajzPBxM6fzLKMeb9qBph0Pbc6t55CmZE+6eh++1Iol7wC\ni56FOSM5df4CP205xkPTN9Dso8VsOn6eSQOCCRvlydDjE1AR6wrf34UjkHw174IhRWFta6omHl8H\nn9WHSXfBT31h6atmPrOYPHomz+wFa3uTkphdw24mYD/yd8Htca0NbZ420xzkTi/MTmuT2vuf9jD3\nH6aHcfDP0PLx/LfxaGDSJIOGg/8Dea/TfJiZdmD566YN2b9kiNwCfz5vxoLm7r0r6HzymioATKAW\nezLresioSlnc6QNEnix7HjiAyl7mMfbmB/zW9XBmxTNd6TBlBd2+XMX/XupecJe+EEIIIcq/w0tM\nb0pGto6NPTQdYHq8rl/JvyclPxeOZqVfRm7JuwR7Xs7sNQGaUmaclPc9piekxUgI+8sEK4N+NMtd\n62RNFn2zMgK4hj1Mb0xqcpFLoE/++yDfbAinmos9td2cqOXqSG03R1wdbLG3scLB1hp7G2tsrRVJ\nKWkkXk/FKi6SYUdXc8KhMT4H/uDE7t28fPkpqFSV5zo15s17G1MldCr8+KkJWA/8Aa2fNj1Gto55\nNyRjvFl+PXBF0WKEqejp4AbVm0J1X1N58Zt2JojPXfzk7H6o3uTGlM0GXUzPmk4tvD2d34BDf5oq\nkU/978bzS7kGsx816Yce9WHgd+A3sGhVG3OnMuZmZQ33fwFzR5nj//WS6fFt3NtUJXWtY/7OSqIc\nfsbffvhy0yt5ZLn5giK/qpaiWCw/gLN1MrnJsZG3tBvfmm4sfboznb9YScN3FtKgqjN3VXOhQVUX\n7qrmTNNabgTWdqey0y1MziiEEEJUVFqbanw1A/K/6S5PEi6aioDtXs75erOhsPMHOPin+bk4MtIR\n7ZxNifWiSE2GswdNJcEMHcbBzwNh969mvrKqjcy8XwCunpBw3tzo2zgUr30ZLkWYnqQGXczcdxfC\nTQBTiJnbI3h5/k5a+1TBwcaafdGxLD94mrhryQVu97bTX2gn6HV2OI94XubVK/8iqt5UbIfNxcpK\nwez7TdppwGATNK2fDFu+gmOrYMB/8g6K0guYUK3xzb0HYAK3wTNufP2ue81YyK4TcgZOMfuzCoNk\n5+gBnq1MemJhAZytE9w3xUzhsO5Tc4wMqddh9nAT9HT/0PxNlPTcYrWaw9jtcGY37J0D++eZVEd7\nV3jsT3MuJcHNy/xNhS83PYNHVoHv/be9WqOlsvwADoo0F1xRtPCuwprnuzFj63GOnovn8Nl4lhyI\nJiklK5/b292JwDruhHh78HT7RlRzyfs/11VhZ3jity10b1KLqYNbmv/AhBBCiIro6jlY9LzpLfJp\nDw//Xv6DuPDlZoxR4145X/dqY3rk9swsfgB37pDpiWna3/QipaUW3nNyIRxSk3L2TNTvYopjLHvD\npAn2/yarnLtbHfMYd8qkzd2M2BPgXjerHH7MgUIDuK0R5xn5y2baNajGyme6Ym+bdV5Xk1KIT0om\nKSWVpOQ0klJSSU7T2NtY4WRjhffPH5Lq3oF9w5/G2soKorrDb0Nh+r2Qch1sHeDBH8BvgNlh789M\nWuuCsfBdV9Nr1fbFnDf/p0NNMZE8CpjcssDBJqg5scHMdwbmb/xKTM4KlNk17WcC+FpF6GGq39mk\nM/7vX+acawaantu5o8xx+3wOIf8oufPJTSkTyNVqbgLmqG0mgLuVYDgvd3UzlTGProKky5I+WYIs\nfwwcFGsuuMIEe3nw+QMtWPhUJ/a/eR8Jk4dy4t3+LB7TiY/7Naddg+ocv3CFd5fso9G7i/h6/WFS\n07ICvKTkVF6Zv5N7v/ybK0kpfLMhnFG/bM6xjhBCiIpDKdVTKRWmlDqilBqfx3JvpdRqpdQupdQe\npVTvbMteS98uTClVMSdHClsMX99tJjNuPsyM7Zk9zFQ6LM8OLzYpXblLvitlipkcXwvxp4u3z7MH\nTFDl096kYJ47WPg2Z9IrEmavXKiUKViSfPXGeb9c0wO4W6lEeSkC3H1Mz56VDZzdz+nLiczZdZK9\neRRsOxWbQP9p66jp6sC8xzvkCN4AKtnbUNPVkboezjSq4UpAHXeCvTzwq1WZevE7sI6LwjZkuAne\nwFQifHyluT+r3wnGbM4K3jI06ApjNppem7/fgVXvZS3TaWbcYK18yvXfqsZ9wM4l51jIjMqRNfIJ\n4Fo/Bc/vLnrabff3zbjHhc+anrc/noKDC6HHx6UbvOWmrMCrtUkfLWkNu0NaMqx42/yd1e9U8se4\nQ905PXBhS8wFn9+EhDfJykrh7VEJb49K9PKrk/n6gdOXeeb3bTw9axvfbTzKV0Na4mxvwyM/bGT3\nqUs81a4hkwYEM+nvA0xcvBcN/PeR1ln/uQkhhCj3lFLWwFSgGxAFbFNKLdRaZ68M8SYwW2v9tVKq\nKbAY8En/eSjgB9QGViqlGmmtb2KSqDKQFAdLX4PQn03wMXyh6cXxamXG18z9h+lVKekUsMIUZTxX\nSpIpOOH/YN73BYFDTHrb3jlwz7NFP/a5Q+ZG2KuleR65NWfFwryc2WPSGas2zPl6414mIG7SJ+f5\nuKWP7Y87xfWUVP7cd4pryakMaVG36PcQl05wvXZLFuw+TVsbTw5tWE3X+VmFObo3qcW4rr7c26Qm\niUlJ9PvPWuKTkln+TI98M4vytWuGKWufu5y/ez14soBCHmDS+R6YbnqHNvzT9Op2eMWMCbx+5eYL\nmBTG1tH0qB1YAL0nmbTHvCpQZqesipd+6Ohuehp/f8yMuTsfZtIp24y59faXF16tze/u3EHzpYaD\nW1m3yGLcGQGcm7dJT7hyFlxq3pZDNq3lxt/PdmXWjhO8NG8nbSYtw87GCjcHWxY+2ZH7AzwBmNA7\nEKUUE/7aQ5rWTB/WRoI4IYSoOFoBR7TWxwCUUjOBfkD2AE4Druk/uwHR6T/3A2ZqrZOA40qpI+n7\n23Q7Gn7L5o026V7tXoZO48E6fQx48GMmQFryCvzxJAz4tmgFGEpC2BKThvbkeqhSQHphxHoTADTu\nnffyKndBnRaw7/eiB3DJiXDxGPg9AO71Te9K1LbCe1Ni9pqgL3cqoLKCflNvXN+1NgBLNm3jsd9s\nOXfF9HROWRPGtIda0dzTw6RIOlXJuzco8RIkXWbSjgTeWLaB2ZWr0d7+BJ/1D6Jdg+qsCY/hizVh\ndJ+6isG1LjEjZSIq9iV++8dw/GtXvnF/BUm8aMYSthhx8+P1lDJjxlKSYPX7Zj/ONcyyWylgUpjA\nwebLibDFJtCP2W96bCsVMr1Bcfj2M719YX9Bh1eh3Uslt+/ywNoWGnQ2gbBMH1Ci7owArlYz83j0\n77wnQCwlSimGhvjQx78O7y3dy6nYRCYPDKaGa85xAW/3CsBaKd78czcpqWmMu7cpTWu64WB7mz7w\nhBBC3Kw6QPYqWVFA61zrTASWK6WeBSoBGVUQ6gCbc21bh1yUUqOB0QDe3t4l0uhblpZiUgxDRplK\ngbm1Gm0CmpVvA8qsk1HpsTSFLzNphxsm5x38ZAhbYnpV6nXIf50m95tJjeNPmwmUC3P+sMn0qd7E\nBB2erQovZKK1qUCZXyCZTWzCdeaEnuTHLceYl+ZM1Ikw2jfoz+P3NOBSwnVenLeTkE+X8mKnxnx6\nZBCq2VDo8WGuw2n+WLuBAcDR1Kosfboz956LxnrN+4xr7wX2LrSpV5UXOzfh1+0RpK14GzuS+app\nJC3Tv3gulr1zzBfoQcOKv212GcFsyjVY8SZUaXjrBUwK49PepKvumZUewO3NP33yZikFA74xgX79\nLiW77/KiSV8TxOceaypuyZ0RwHm2NBf7rhm3NYDL4OJgy6f9gwtc542e/lhbKV5bGMpvO05gpRQN\nq7sQULsyne6qzuP33HVDzrkQQogK4SHgB631P5VSdwMzlFKF5NVl0VpPA6YBhISE6FJqY/GcPWgm\nmvZslf86bZ83Y3tWvw/75ph1/R8wY50yelBKWuQW87hnJnR8NWsu2OzSUs30AQ26FFxopWF3E8Ad\nWQlBjxZ+7HOHzGNGMRDPluY4CRdvnFQ5Q/xpSLiQb2n15NQ0lh6IZsbW4yzcG0VSShqNa7iS5lKb\n4dXseOKxrAC0Z9PajF8Qyqw1G/msygWi963hQN3TtPD2wN3JnoTrKTw9axtXd61hgBt8NnIAHvVr\nQ1j6n+LZgyb9FbC3tWbk3Q3QOw/DeWiZVMSKmrnt+sl8iV4SpeOtbExJ/dTrplfMs2XpFDDJoKwg\nYBBs/DfERcO5sNIJsuxdzXg/S+X/AHi3Abeb+AJA5OvOCOCUMt/+rJwA58NvzDMvJ8Z392NQkDe7\noi6x59Ql9kbHsuPkRebsOsnk1Yf4tF8QA5t7oaQEqxBClBenAK9szz3TX8tuFNATQGu9SSnlAFQt\n4rbl06nt5tGzRcHrdXjF3ATvn2fmG1v6Kix7DZo9DPf/q2RvwBMvmSAkeASE/mLmUes96cb1tnxj\nKjgWNmdW9aamByZ8edECuLMHwMo2qzJkejBE1DZolE99mpg8CpikW7gnirGztxEVm0BVZ3tGt23I\no63qEeLtgZr1a9Zcbuk8Ktkz7eHW7POMhNXgHn+Ynl+uIBVr6lUxqZQRF6+wrJkNnAKPOun3QhnF\nK87uz2ozwIUjqPOHTdXFs/uLf/90OtT0Lub1O7hZ1rZmXOWfL5igoLQFDoH/TYG1n5jAsbDxjOJG\nSknwVgrunMFWzR4CZW164cqxBtVceDDIm3fva8b80R059k4/lo3tgpOtNQ/+dz0dpqxg24kLZd1M\nIYQQxjagoVKqnlLKDlOUZGGudU4CXQGUUr6AA3Aufb2hSil7pVQ9oCFwk10dJehaLHxW3wQu+Yna\nboowuNcvfH/uPmZsz1P/g6e3QMvRZmzRgrEm5bCkRG0DtPnGv9lDsPMniD+Tc51LEaZHsGEPkyJZ\nEKXMuJ2jq01hlMKcO2QCnIyCI7WDzX1HHmmUWmsuXk1Cn95jXsiWmhcTl8iQ79fTb9pa3J3sWDC6\nI9EfDOSLQSG0rFvFk2htTQAAIABJREFUfInrWiffKpT+tjEAOKpk1j/izUd9m9PC24Oarg4sfboL\n3WommfFx9unDMit7m7FyMQdy7ihsiXns88/054sLfw+y2znDjFcLGFS87QpjYw/9vzZjLUtb9aYm\nuM64dyzpFEohblKRvvpSSvUE/gVYA99prT/Otfwl4HEgBfOh9A+t9Yn0ZanA3vRVT2qt+5ZQ24vH\nuQY06gm7f4Mub93+qli3oLtvLXaN7830zcd488/dtPpsKVWd7UlOTSMlVZOcZh41Gp0tuaaWqyNP\ntruLJ9s1pKZrOZ+PRwghKiCtdYpS6hlgGeYz8nut9X6l1LvAdq31QuBl4Ful1IuYgiYjtNYa2K+U\nmo0peJICjC0XFSgvHDVpffvn5z9vU/ROU+SjuBkh1ZpAr09MALHmA3Mzft+/btxP7Ek4tcP0XNk6\nFW3fJzebgKlOCzNXWujPsOlLU64dzHizP18w6/SZXLS2N+xuJvWO3GzGRBXk7AGT1pfBrpK54U8P\n4FJS09hw9BwL9kaxYE8Uxy9cYVGVpbS0q8nC7WfpeBdsjjjPi3N3cOV6Cu/dF8j/3dsUO5s8hk+4\n1jHzaiXFg71LzmXnw8w56lTudojk7rs75Vy+PSLneERlZXrhzuYK4A4vgRoB4H23SYE8vMSkxRZF\ncqIpAOPb11SgrMgCh8LyN0zvajnN4BJ3nkIDuCKWSN4FhGitE5RSY4BPgSHpyxK11qVYJqgYgh41\nlX7Cl5uyvBWIjbUVT7S9iyHBdZm6Loyo2ARsra2wsbLC1lphbaWwSv8wUkqhgG0nLzBx8V4+WLaf\nIcHePN+pCSF1q5TtiQghhIXRWi/GTA2Q/bW3s/18AGibz7YfAB+UagOLK2Pus+NrTdCTO9BJijep\nir638H1sh1dMQYoN/zS9ND0/MceJPwPrJ8GOH8z8UZWqQ/tx6VUM7QveZ+QWM4myXSWTxuj/IGz/\n3vT+OXmYL3CPrTYpfUVN6arf0dy4H15WcAB3/YoJOoOG53zdsxV6z288O3MTv+2M4mLCdextrLi3\ncU0ev6cBwdtPsS2pDqN/25K5Sdv61fj24db41iyg5HpG++NOmaA4u/OHwTPE9KhFh96Y/hl74sbq\njdWbmjnIMn7fCRfh5Cbz3oMpsrLuU7h6vmhVGPfMhGuXIXh44euWd/4Pwoq3zPucUWlViDJWlB64\nQkska61XZ1t/M3CL5YZKScNupgTsrhkVLoDL4Opoy2v/z959h0V5ZQ8c/x6qgFIUEBXsoKKxYu+9\nxGgSTaKuSUzduEl2U37p2WRjNj2bbopJTI+mqzH22BI79t6wYsMCqIC0+/vjDhERFBEYBs/neeYZ\n5p13Zs7gyDvnvfee07fwc7C3H0nmvYXb+GzpTr5esZtO9UJ4vHdjBjSurmvplFJKnS/ZkcAlx9t+\nW3lHHQ6sBgzUiCn6a4jY2TCZabB0rB0xcnOHFR/bCpfNR9qqdYvfhRmPwpJ3ocuj0HxE/uvmstLt\niF2r285u6/QQrP8eln0Are+ya+8i2tnKmYXlVRFqdyRj60yeT7+BCSv3EBVaidGdIunfuPrZtj8J\nW+11nmbICUFNCUn/hEVL/2BAi45c1yyCPo2qUdHb0ybCSw9SrecotjS8hgXbD+Pn7cHwVrVxc7vI\n8fmvZt77z0/gErba5tduHnYdWm7ZWTbRjL723O2hjWHVF3DqkK24uX2mnd6aUzmwQX9Y8LI9Ad58\nxIVjy86EP9+0I6G1Ol14X1dQKQza/sOO6ipVRhQmgStMieTc7gCm57pdQURisdNDXjbGTLrkKIuL\nm4edF7/47cKXBXZxkaH+vD00huevbsb4pTt5c+5mBn44n6uqB/JY72hualkLD/fzl0Impaaz/kAi\n6+IT2XAwkVqV/RjZug41Ags5lUUpVb5dqLKecm0nD5z9edf88xO4nAImNS5SwORiRKDPCzaJW/Y+\nILZoRNfHoLJjbV1kXxvD3Ofh1/th2wwY9u35z3VwHWSmQs1cX09CG9lEZtlHtlhIRgoMejf/xt35\nyM42zNpykPiEetxxcj5f7VhAvfqNWb3vBNd8tICaQb7c3TGSOzrUI+zI5rOv6bBg+2H+79fTrKgA\n3/T0JPqaPIOwRzYBBglrSoOq/jSo6k+h5SQTyXnWwZ0+anuvhTS0yWfspzahykl6k+Pt7bwtHao6\nKmce3mi/G22dZq9zRurCmkGl6rBt2sUTuPU/2lG+nFHV8qBv2RokV6pYq1CKyEggBuiaa3MtY0y8\niNQF5orIemPMzjyPK70eNy1uhkVvwppvofPDJftaZYi/jycPdG/IvV2imLhyNy/P2sTILxbz6KTV\nhPn7YIwhZ/nc8dNn2Hsi5a/HVqrgwcm0TJ6cspY+jcIY1bYug5tGaJ86pa5Ue5fAhJvsOqImQ50d\njSpuJw+Cf7j98r1rgR29yi1+pU2wiiOBF7FTGmvEQI2W548miUDd7lCnm60kvfht2yy7cp7iKfsc\n7fQi8lQm7PQwbP7VJiTdn4bgKIwxHD11hsTUdE6dyeTUmUxOp2dy/PQZth05yZbDyWw5nMy2I8mk\nZmTR3r8ud3hD7HXuVOnWk4ysbKas288Hf27n6alreea3dXwZNo0bxYs5Bzzp5JvB1yt28c8fYqkf\nHE6mdxWis7ae/94POcoDhBWhsmGl6oCcX8gkp5VBcBT4BtvkOGHL2eqJiXvsdVDtcx8X6ijOcWQT\n1O4EO363jaxzkl0ROwq3dqJ9zoKacptsOy22ahNbd0ApVSIKk8AVqsyxiPQCngK6GmPO5Gw3xsQ7\nruNEZD7QAjgngSvVHjdV6kGtjnZxc6eHys/ZoULydHfj5jZ1+VtMHX7bGM+Xy3eRlmHXzAt2/Vx0\nWABXVQ+kaY1AmlYPokagDzsSTvLl8l18sSyOYZ8tIsDHk+ubRXBTy1r0aBCGZz6jeEqpcmjnXJg4\nwo4A5P2yrMqHkwfBvxoEN4AtU+20OzfHCTtjbAXKCzXAvlTidvFRHRFo+3c7lXLNN9Dj3xhjOJSc\nxvGUM1TbshAfv3Bm78rkdPpuUtIzSc3IIjXDm/6Ve+KXEs+DG5qxfeFUdh8/RUp6/rViRKBOlYo0\nrOpPj6iqtK8TzOCrasAHH1Pl4ELgXjzd3RjSoiZDWtRk2+FkvondTf3VH7I+I4wBH/6BmwjZxjCg\ncXW+HdURj8ltYV8+xUUPrbcFPvyLUGLd3dMWZ8s7AnfUkSiGNICMNPvzgTVnE7ic1gN5EzjfynaJ\nyZFNsPtP2ww9b3PxBgPsiN6uP+ySlPxsnmLX4A397Ir7fqVUaSpMAvdXiWRs4jYMOOcvrYi0AD4C\n+hljjuTaHgSkGGPOiEgwdhH3q8UVfJG1uBkm3QN7FtkzTVcgNzfhmqvCueaqwh04IkP9eX5gM54b\n0JR52w/z5bI4flqzj8+WxlHFz5shzSPoWj+UlIwsElPSSUrLIDElnaY1ghjZujY+XldGy0GlyrUt\nU+HH2+wX+5E/Q8VQZ0ekSkLyQZsA1O1mT3YeWmtL4oNNGE4durz1b0W0Kz0At6B2+C/5nGvXxLDu\n4CkSU9MBw8EqS5ia3pBbxy0473GPcR0BFTyplZ1JVGgl+jaqRu0qflTx88bPy4OK3h74eXkQ4ONF\n3eCK+c8uqd/Hrp/PM/oUVdWf565uClsTyGzUmVnRPZi//TAhlSpwf9cou0YuvLUdAUw5Zqtv5ji8\n3paoL2qiE1ADkvefuy1hG3j6OZJCA16V7Dq4Fo7SBCd22/WG+RVxqRptp1B6+tpL3iS9dmc7LXPr\ntPwTOGNg4etQJRIaDS7ae1JKFcpFv1UXskTya0BF4AdHYYycdgGNgI9EJBvbc+7lPNUrnSN6MEx/\nFFaOv2ITuKJycxN6NgijZ4Mw0jKymLn5AN+t3MM3K3YzbtGOs/uJ4Oftbqde/rqGf3SO4h+dI6mq\n7QyUck3rvoNJo6F6C/jbj7YHmCqfTh60yVvOF/i4BWcTuL8aeJduAvfbhniGf/4nvbMb81PAYtpk\nrqNxTHcahwVQx+0IYXOTad9lICub9MfX0x1fLw98HNcVPN3OFhspqsg+sGKcHZ2q3+vc+9IS4eQB\nPMKi6d2oGr0b5VlfH+5ojr1nsV2TB3Yd2uGNl1ZQJS//GrYaaG5Ht9o1iyKA2KqcuQuZJO6xyVt+\nhWBCo2H5x3YdXb0e50+T9PC227dNB5NPG4bts2xSOviDsyO2SqkSUahhkUKUSO513oPs9sXAVZcT\nYInw9LWVqha/DUF17Lx4Heq/ZBU83RncNILBTSNISc8k7ugpAnw8CfTxoqK3/Wgt3HGEN+Zu4fkZ\n63l59kb+FlObuzrUp12dYK2CqVRZkLvAQUH3L/3AltGu3QmGTTi/75QqP9JPwZlkW8CiYlX7pX7X\nAuj0oL1/f6wtpV61COu2isAYw+u/b+axyatpEV6Zl25+HPPNz7xWawPc+Jjdac1KACJb9YbQEiqs\nU7uTTWi2zz4/gTviWHcWGp3/Y2u0tL+z70fapCu0kV3DlplmR+CKKiACdsw5t9XD0W3ntjuo1ty2\nUsj5f35i9/nTJ3OENoasM7aITYOn89+nwQA7TfLgGnsyJ4cxts1AYM3ib9ytlDrPlTuvreezkHrC\n9ps5cwr6vVToylTqfL5eHjSpfn6zzq6RVekaWZVth5N5e/4WPlsax2dL46gXXJGRrevwt9a1iQy9\nhMpbF7DvxGl8PN0JrljA4mql1Fnpp2DuC7D8QztC0OkhO8qQ+8TKjjk2cTuyyX5xGzIePHUUvVzL\naSHgX91e1+kKKz87O3XwwCrb1LmAnmxbDiURHuRry+RfwNFTaazed4I18SdYve84mw4l07haAAOb\n1KBfdDWCfL1Jy8ji7gnL+Gr5Lm5sWZPPRrbH18vDNlZePu5sT7J9S6FCwPkFUIqTp48dkdwxC3jl\n3PtyGmAX9PqevjBqOuz50+57ZJNdRyZuZ0fnisK/hq2smXYCfCrbxDs53hYwyVGtua3OmbDVNhU/\nsfv8tW05cipRIrb6Z34i+9i4t04/N4HbtcCOzl79hl2fp5QqUVduAufmDte8Y88kLx1rv8xc844O\n+5eQqKr+jL2pDS8NasHPa/fy9YrdjJmxnuemr6dL/VDeHNKKlhFFP3M6Zd1+hn/+J9X8fVj5WH8C\nfLTZprrCZWfC6QRbmCDvaPeOOTD1QUjaC42vs6MqE260Z+A7PWi/iP7+H7tfUG244UvbtFlHzcu/\nnBYCOW126nS1fdT2LYdaHWwPuLyNoR1i9xyjzesz8PX0YEjzCG5tW5dukVVxcxOysrNZuusok9fv\nZ8r6eLYeTv7rcRFBvjSqGsDsLQf5NnY37m5C53qhnDqTQeze44y5uilP92tydtZGi5vtcXvdRGh/\nn23gHdG25E/C1u9jpwke22kLouVI2GLXhgVEFPzY8Jhzp51mZ9o+cJczFTkgVy84n8pwdLu9HdLg\n7D7VHW0ADq6x/5dPJ5zfQiBHcIOzSWVBzbp9q9jiRSvG2YQ0x/E4+7em+d+K/n6UUoV25SZwcLYH\njVdFWPiKPZN13Tg9e1SC/H08GdWuHqPa1SM+MYVvY3fzv9830/rVGdzbJYrnBza95OTrnflbeOCn\nlTQOC2Dz4WRu/3opP97ZWadoqvLrzzftF8A2d9kqdnnFzYPpj9n1MH4h9stteBt7xnzN13Y9W3AU\n3DYDaraHrAzY8KN93p/vtM9RIcD+fWx9V4GjLaocyjsCV7ujLXqxa4FNNjJSCixg8sSUNVTx8+ba\npuF8v2ovXy7fRc0gXzrUDWHutsMcOZmGh5vQPaoqt7erS8uIyrQIr0yVivbzlZWdzfLdx/h1Qzy/\nrt9PfFIqP9zRmaEt8rQXCm1kY1j9FTQbYROo0pi2F9nLdrmd/6I94evlZ7cf2WRjupRjjpvH5a8j\nzalemRRvR0VzmokH50rgqtS333EOrD47YlbQFEpPH+j8iC26ciEdH7DVQHOrUh9a31lwewGlVLG6\nshM4sH9wuz9p/xDPeQaO74SBb51dsK1KTI1AXx7pFc1dHerz9NS1vLdwKz+s3sOb17fipla1LpqA\nZWVn89DPq3hn/laubRrON6M68v7CbTwyaTVvz9/KA91LcDqNUs6yb5kdHQNY/A60vQfajbZn4JP2\nwaynYNNku76313P2y+2+ZbaCJNgvjl0etX0wc75suXtCs+G2ifLWafZMfstbtVH3lSjvCJy3v23Y\nHTf/bOXC8PMbeM/ZcpA5Ww/x5pBWPNC9Ie8MjWHSuv18sSyOudsO0yOqKoOvCqd/4+oFnqRzd3Oj\nfd0Q2tcN4cVBzTHGFHwcaDESpj4AS9+3t0ujpUVQHZvg/PG6nUp63Tib7CRscU7Ps7+aeTsqUR7d\nCm6eULnO2X3EzSZ3B9cU3EIgt+5PXvx1o/rai1LKaTSBy9HxX/aP2vRH4OMe9qxzj6ftWWhVogJ9\nvXjvxtaMaluXeyYuZ/jni3hk0moaVvWnQVV/GoT6ExlaiQoe7uS0GzcG3p6/lSnr9/Ng94a8dl0L\n3N3ceLhnIxbFJfDIL6toU6sKHeqGOPndKVWMjLEJWsUwuPFLexZ84au2yEjDq21xAWNsYaYO9597\nNvx0gqMBcz1bpS4/4gYNB5bOe1FlU/JB8A44O7oEtiLlH6/bUTmfyhB0bhNtYwxPTFlDzSBf7ulk\nP1s+Xh4Mj6nN8JjaRQ7lgifxmgyBmU/CorfsSYkapXTStcfT9vcxaTSM7wNt/m7/bxVUwKQk+YXa\n957TzPvoNjsSlrcoUbXmdh3jMUel6AslcEopl6AJXG7Rg+0f5nkv2AXSmydD35fsgUKVuJhaVVj2\nSF8+XxrHvO2H2Xo4mS+Xx3EyLTPf/d1EeO+GGO7tena6iIjw2cj2tHp1OjeO/4PVjw0gpJJO6VDl\nxMafYf8KGPSenRYZ0daWIl/4qp0W2ega6Pti/mtx/EKcM0qgXMvJA7aJd251utrP2JapUK/neVMF\nf16zj9i9x/lsZLv8e6iVBG9/iL4W1n5rp1N6+pbO64KtSDl6Ecx43K4PBDuFsrS5udtqljnNvBO2\nQlg+1UGrt4Bl79v1e16VbBKulHJpmsDlVSEA+r8KzYbZRf4/3W4Xb/d9UQuclAJ3Nzfu6FCfOzrU\nB+yZ3UPJaew8epLMLDv6JmIv1fx98q1gGejrxY93dKb9/2Yy4vNF3NWxPpsOJrHpUBIbDyZx9PQZ\nIkMq0SjMn0ZVA2gY5k/HuiFa+ESVbZlpdupk1SZ23U+Oqo3hhi8gO0v/RqnLd/Lg2emTOcJbg4eP\nrWaYZ/1bZlY2T01dS3RYADe3qUOpanGzTeBKY/pkXt7+MPh9iOoPmyZdXjXJyxFQwyZwmWfgxK78\nTzjnFDLZs8gWKtL14Uq5PE3gClK9Jdw5F2Y/A0vfs2tLhnxSumf5CistCVZ+Dq1GlbspnyJCtQAf\nqgVcWunyFhGVefeG1tw9YRlzth5CBOoFVyI6LIB2dYLZkXCSyev288mpnQDUCPTh5zu70KZ2AZW3\nlHK25eMgcS+M/CX/RE2TN1Uckg9CvTzrhz28OR4cQ+VDf7DdK4rcE3C/WBbH1sPJ/HJXl8tvln2p\naraHPi/a6cPO0uias825ncG/hh2VP7YDTPa5BUxy5BQyST8FQQVUoFRKuRRN4C7EzR36vmAbU854\nDD6/GoZ/BxVDnR3ZuWY+aSvLnTwI/V52djTFLzkePmhvS5nX7Vboh93ZoR5RoZUI8PGkQag/Pl7n\nf9yPnTpD7N5j3PPdcrq8NZsPbmrDbe3r5fNsSpWg7CxbOXL1V7baX5Oh0O3Js0VEUo7Bwtehfm+o\n18O5saryKzsLTh0+ZwTOGMP7C7exZUd1XvHzpO2Eo7ReNZcn+jSmbe0qPDttHW1rV2Fw0/DSj1cE\n2t9b+q9blvjXgOTJtpAKQEjU+fvkFDLZuxgCa5dqeEqpkqGdqwuj7d/hpm/gyGb4tJet5pSdVTqv\nnZFie7wUJG6+Td78QmHFx3YRc3kTt8COMq755pIeJiJ0jaxK8/DK+SZvAFUqetM3ujorHulHp3qh\n3P7NUu7/fgUZWdnFEblSF3Zit11z+3ZT+GYI7FpoRxVix8O7LWDZh7bE/4JXIP0k9H7e2RGr8uz0\nETBZfyVw6ZlZ/H3Ccu77IZa9dYeReGcsjw3qyNr4E3R/Zw4Nnv+V+MRUXh7UQtu2OEtAOGRn2OmR\nCFQpoEBRNcc0Si1golS5oCNwhdXwahj1G0y4CcZ1BXdvqFzXNvPMqeoWHGWvC7tA+MBq21CzoFLd\n2Znw1bVwcB2M/Nk2Uc0tIwWm/su+/i2T4YMOMOtpGPH95b3XsmbvEnu9bYad518CPamCK1Zgxj+6\n8/jkNfxv7mbWxp+gT6NqpKZnkZphLxU83Rl0VQ261A8t/alCquxKOWb/3/nXsNVsvStdeP+sdFuq\nf9UXsHOe3Va/px3tj+pvP99HNsHMJ+zIf+yntkluy1udUyhBXTmSHS0E/Ktz5GQaQz5ZyJ87E3iy\nT2OeH9gMNzfhsXD4Z9cGfL4sjv/9vpnrm0XQLaqqc+O+kvk7WgnsnGunR3oWsNwgZx2cTqFUqlzQ\nBO5S1GgFf/8Tts+0882P7bT9krbNtGfAcvgG28ICvcecPeuVmzGw+G2Y86wtPXz7TLsgOq8Fr9j+\nTX6h8O2NcOuUc/vTzXvRnsG/9Tdbda7z/9ledjt/t5XCyot9S22z4rREO70ssk+JvIyHuxuvX9+S\nlhFB3D1hOX/sTMDdTfDxdMfH051TZzJ5a94WqlaqwJDmEdzUqhbVA3zZeDCRjQdtgZRdx04xvFVt\n7u0ShZubnpEu9+Lmwy9/h5Sj9oTL6i+hxzPQfISdtpTDZMOhdbDxFzuSfDrBNuHt9gQ0/9vZ/lo5\nQqNh5CTYNt22DfDys1MqlSpJJ20T79PeIbR9fQaHktP4dlTH81oB+Hh5MLpzFKM75zNdT5WunF5w\nJ3ZB5AV6szW82vawq925dOJSSpUoMcY4O4ZzxMTEmNjYWGeHcWmyM21xgaPb7RTGY9ttud6UY9D7\nv9Dm7rNVn7IzYdojsHI81OkCuxfZs+/DJp5bhGDPIvhiIDQdBj3+DZ/1gzPJMGqa/XJ3YBV80tNW\n4brmHfuYzDPwfhtbLeyeP8/vBeOKTh+F1+tB1ydg6VhoNAgGjy3xl82ZQunpfvZLeEp6JtM2HuD7\nVXuYuiGe1Ixzp9HWDPIl0NeLdfGJdKgbzCcj2tEorIwWldk+25abzlttThVOVgbM+y8setuOug/5\nFDLTYebjtqBAWFObcJ08aE867FoIqcdB3KFBfzuaVq9n4QqPZGXY6ZPltPS3iKw0xsRcfE8FJXyM\nXPExTPs/vus2m2E/bGfmvT3o00j/RpRpOcdIgPb3Q5//OjcepVSxudDxsRx8wy8D3DzsdMrKdSHK\ncQYs5ThMHg0zHoXdC23fJndP+PE2m9x1fAB6Pmuba/72kJ2C1e8l+9jU4/DzXXauev9X7ZSsWybD\n+H52SuWtU2HKP+3IXO8xZ+Pw8LYJ4/cjbVXK1neW9m/iXKcT7HQOr4pFf459S+113a5wfIedepad\nWeLJae7ELYevlwdDW9RkaIuanDqTwfSNB0hOy6BJ9UCiwwKoVMETYwxfr9jFAz+upPnL03im31U8\n2js63+dzmj2L4duhthn0iO/yHyUub45stuXGTx22J1ZSjsHpY/b/VpMh0PSm80fB8pOdBfuX28JB\nB1bZRKzvS2ebHt8+Gzb8ZEfXJw6z2ypVt/3X6naDut0vvQiSu2e5Td5UGZN8ENw8+GxdMnWqVKR3\nwzBnR6QuxrcKeFSwbUZC8qlAqZQqlzSBKym+le2o2tL37Ze5j7rYEv9HNsHVb0LM7Xa/mDvsyN2y\n9yG4PrS6HabcD6eOwB2zz66nCapjk7jP+sNHne0f6xu/tlMLc2s40E6RmP8iXDX0/PtLS0aKjTOs\n6eWtydu7xK43rN4SGiXA+h/s6GSdrsUXaxFU9PbkhpbnryUQEW5uU5c+Davxzx9jeXrqWr5YFke3\nyKo0qxFI0xpBNK0R6Lyec8bYz2PFqjYx+GwADB1ffhs8J2yxU5E3/mLfb6Vq9guPb7Bds5q4F+aO\ngbnPQ53Otr9aWFN74iHnkpVhp0punwk75kDqCft/+YYvbCPh3ETs/7uGA2DH7xDS0Jbw1gIPyhWc\nPEiWX1VmbzvCE32itTCJKxCx6+CO77R/b5RSVwRN4EpSTonjmu3syNvxONuGILL3ufv1ecH+8Z32\niC1ssmWqrTZXvcW5+4U0hJt/gS8G2WlY+fWeEbFNxz/qAgtes4URCiMjxY7MxM2zo4dRfW3J8pyR\nhUu1fJydPnbyIBxaD2FXFe159i6FGi3t6GL9XrYP36Yp5ydwxsD0R8C/OnR6qGivVYyq+vvw3e2d\nGRGzj3fmb+WnNXv5ePGOv+5vVzuY5wc2pWeDsNL9krR1mh1BGvi2Tdom3AgTh9uR3tZ3lV4cJSln\nvdnid2DDz/Yz3OlBaH+fTd7yOrEL1n4H6ybApHsKfl7fYPs7i+xjpz9eqOeip69ze0OpUiMi/YC3\nAXfgE2PMy3nufxPo7rjpC4QaYwId92UB6x337TXGDCqdqAtw8gBHCCLbGEbElHJTblV0AY4ELljX\nJCp1pdA1cKUlIwXOnCp4+tSZZBjf147Q1esBf/vp3CIIuaUl2WmJF1o/8+s/YdWXcO2H0GxYATGl\n2kRrx2xbLCUrHdy97Bfe1BN2LV1kL2g0GBoMKHwyl5YE7zSD0Ma25UJUP7tG6FJlpMDLEXZef6//\n2G3f3wz7lsNDm8/9/az7Hn65CxC4a975ya+TGWOIT0xl3YETrNp3nHGLdrDvRArdo6rywsBmtK8b\nUvJBZGfBhx3s9T+W2mmo6afhpztssYx290Gf5wv+3BXVyUMw60k7GtzyluKfspmZZj+/+5bb6/3L\n7WfQ08+2ACmwhEgSAAAgAElEQVQoccvLGIhfafsOZqTaz19mmv191WxvTyQU9+9GufQaOBFxB7YB\nvYH9wApguDFmUwH73w+0MMbc7rh9yhhzSXPMS/QYObYNc08E8pD7A6x5YkDJvIYqfr89DDvnwD/X\nOjsSpVQx0jVwZYGnr70UxNvfTjVc/K6tJnmhL4oXOvOfo/+rtkLl5NHg4QWNrz/3/pMH7cjLgdVQ\n9Spo83eo191+UXX3sqNxm6fA5l/tJSACBr5lR8EuZslYmwD2fQHW/2iLj/R42k4DvRTxK+16t5rt\nz25rNNjGtW+5HdkEu4h7xmN2mmXSfpj2MNwxp0x92RYRwoN8CQ/yZUDjGjzSM5qPFm3nhZkb6fDG\nLAY2qcED3RvSLbIEWxSsnWCnFN7w5dk1hF5+tsfhzCdg6Xs2YRnwevFN+Tu41n7OUk/YkbHYT+0U\nxZa32II0yfF2hPbgOji83q4jveYd+xm8EGNsorbmWzs98kyS3R7SyE5rDG9jR6kLk7jlEIHwGMAl\ncwnlHG2AHcaYOAARmQgMBvJN4IDhwLOlFNsly04+wPqT1RjRS0vNu5Rez0Jn5888UUqVHk3gypKA\nCJt4FQePCjBsAnwzFH66034hbjjQ3ndgFUwcAWdO2n0a5HOmtU4Xe+n/qq2iN/1R22i42QibmBVU\nVCHl2NlqkdWa27VWyz+Cxe/B1f+7tPew11HAJKLN2W1Rfex72TzlbAI34zH7Xga/b6fO/XK37bHV\n6rZLe71S5O3pzj+7NeT29vV4Z/5WXpuzmakb4qke4MOImNr8rXVtmtUIKr7plZlpMP8l2wqjUZ5Z\nWm7u0O8V+5lZ/LadrtrnhctP4rb8Bj/faT8rt8+CwAhY94P9t5n2f/aSw6sShETZJNPd254syO/1\nM1LsutI139opQ56+9v00vg4i2oJP0OXFrNSlqQHsy3V7P9A2vx1FpBZQB5iba3MFEYkFMoGXjTGT\nCnjs3cDdADVr1iyGsPORfgq39JPEZwdyX6vaJfMaqmR4++ffikgpVW5pAleeefnZUb2vr4MfRsGw\nb22iM/kftoLl7TOhapMLP4e42cp5f/8DFr4Gf75pCzkMeB2iB5+//59v2i/Z3Z+ytytVs60Q1nwN\n3R4Hv0uYKrh3sW2ZkPtLube/nWK6+VebZGyfBRt+tG0GQhvZdYKrvoDfn7Nf7HOPwBgDyz6w8Q/5\ntEx82a/o7cmTfZvwYPeGTN0Qz9crdvPWvC28/vtmalfxo4qvNz5e7o5edB40qRbA6M5RhAddYDQ3\nPys+geT9cO0H+SdGItDrOduKYulYm8T1eKZoSZwxdv3ZnGfttMNhE2wiD9DmLlsd9eAaWxikcl27\nPjKotv2szfkPLHrTfi7b5FmTl3LcrtnbvwJqdbJrHaMHX7xxtlJlwzDgR2NM7v4jtYwx8SJSF5gr\nIuuNMTvzPtAYMw4YB3YKZUkEZ5IOIECl4AhqVi7i2mellFKlQhO48s67EvztR/hykB11y86AiHZw\n09eXlkx5VLD96KIHw+R74Ydb7Mhd/1ftyCHYaZkrPrYl2XNXw+r4L1j9FSz70D5HjswzdurevuVw\n27RzzyBmZ9rtV914fiyNBsG2GbY9w28P2iQvZ/qIiE0uP+xkk4FB79rtGSm2uueGH+3tyffaqYP5\nJShJ+21rBw9vO90vrClUa2rL7l8soTm2wyYjl9jmwMfLgxta1uKGlrU4eiqNH1bvZd62w5xOzyQ1\nI4uTaZkcPpnG1A3xvDpnEze1qsVDPRrRMqIQ5eXTkuCP123iW6dLwfuJQL+XIesM/PmGXQPZ9dFL\neh+cPmqLyWz82Y6KDf7AVnLM+zrVW+S/TrHnM3aa54zHbH+1ut3s9qR98PX1cGIP3PjV+aOISjlH\nPBCR63a4Y1t+hgH35t5gjIl3XMeJyHygBXBeAlca4nZtpx7QrFG0M15eKaXUJdAE7kpQIRBGTrIF\nQIIjHdPlvIv2XGFNbZGQJWNhwcswti10ewLajbYjdNmZ0PWxcx9Tpb6tyLfiY5vMefvbZO/7W+w6\nJoD5L9vqmTkOb4T0U1Crw/kxRPW3DZG/v8UmJzd8ee6aqdBoG8+S9+xaK78Q+G4kHN5gR5U8Ktii\nGkvH2gIXuaUctyOWyQdswZnNU87eF+AoqNLqVvscuR3dDrOftollgwEw9PMi/46DK1ZgdL1TjD7z\nu01McyW2u46e4p0FW/lk8Q6+WbGbrvVDebJvE3o3zKeaZUYq7P4DYsfbNWg9/3PxFxeBq9+wBW3m\nv2D/nSJ72/WFlS7QE8oYWDfR9kc7c9Im6p0euvR1iOIG14+DT/vAD7fCXXNtov/1EFtwZeTPULvT\npT2nUiVnBRApInWwidswYETenUSkIRAELMm1LQhIMcacEZFgoCNQTHPoL92KDRuoB3Rq3sxZISil\nlCokTeCuFL6VYdRvxfNcbh42EWt8nV3HNPtpu3bp6DZocUv+xUo6PmCToZWf2xHA72+2X/Rv+AJ2\nzrWjcy1G2uQLzq5/y1nnlve91Olsp+C1/Yej8EQeXR+3DZUn3WPX5ZlsOxJZv5dNNvYtsVP8wlvb\ntVNgE4Rvb7CjPDmJwplkOLTBrq3bNMk2Zl/0pk1OWt5iR/YWvGqTUw8fO/q47jv7/m788vxE72KM\nsc3dZzxmk6idv9uKpI7qpXWCK/LmkFb8Z8BVfLJ4J2/N20LfsXPpXC+E5wc2o2u4py3qsW0m7FoI\nmal2nViXR6FaIb+YiRtc8669XvU5rBxvt1eqZkfNQqNtuergSKgSCSlHYeqDtgVFRFtbhORy+hF5\n+8PwCfBxd7uGM+WYfQ+3TYeqjYv+vEoVM2NMpojcB8zEthEYb4zZKCJjgFhjTM4ZoGHARHNu2edG\nwEcikg24YdfAFVT8pERlZxt2794G7lA5rLYzQlBKKXUJtI2AujzG2MRsxmN2NOy+lbYXW36+vMZW\nJUxPsX1rhk2wyUDKMXivlV33dMuvdhTox1GwbwU8uDH/59o2wxZHufHrgtsbbPgJfrrdvsZN39j1\nVjnSkmBcF8hMt+v7KgTAxGE2mbzhy/x7eBljC7rMfwn2LYVK1W2ClJZkE9fuT9lEK/ZTOwWzfi/7\nuoVN4jJS7OPWTrCPbTocfr3frh8b+fO58Tucycjik8U7+H7WLIZnzWCUz3IqkI4Jqo1E9rU9y2p3\nuvREMndMh9ZD/Co4uNpWLT22E3Iv4xF3m2D1etY2pi+u6p+7FtrR0KA69v0HllDxBuVUrtxGwBlK\n4hj5x44jrPnkTv5eaSVeTxU0A1QppVRputDxURM4VTzST9lpegERBe8TNx++GmzXYg359NxKljlJ\nz5BPofEQeLMR1OpYtP5xOYyxhVCqNc8/yTu4Fj7tbRMc3yqw/ns7etTy1os/7675tmCLRwW7bitv\nMZhVX8Cv/7JruIZNsOvAMs/YNXIJW2xVSL8Q8Au21zn92A5vtKOHXR+1idD+WDsq6OZuR+Jyj6Kd\nOmL7nsV+CnHzyBQvJma05fXkThz2q0+fhtXpF12d3g3DCK5YxAQuP1nptkXF0W32kppo+6351yi+\n18hxdJuduqkV1sotTeAuTUkcI0dPXE6/9Y8wsHoq7vcuL9bnVkopVTTaB06VPK+K9nIhdbvBfasc\nRT7yNCFvOcomPbOettPvTh7Mf/rkpRCxSWBBqjWD/q/A1Afs7R7/vnjylvO8dbvbS0Fa3mpHpqbc\nB+O62imcx+POHbnKyyfo7DTPHOExtlro19fD51dDq1E2qTm0zv6OwE5t7PFvPFrdxrXu/mSu3svM\nzQeZuiGeL5fvQgS61AvlwR4NuaZJOG5ul9kewN3LMYUy6vKepzBK4zWUusIt3X2Uf/mext0/3Nmh\nKKWUKoRCJXAi0g94GzvH/xNjzMt57n8IuBPbyyYBuN0Ys8dx363A045d/2uM+aKYYleuqEq9/Le7\nucOA/8GnvWzLAzi3gXdJaTkKEvfZEbJODxfvc7cYaQuZLHnXjkxGD7aNpkMa2BHBlGO2auPpBDsN\nM3pw/tMEg6NsH7Vvb7SFV0IaQp2uNgENa2rXnbl7AlARGNWuHqPa1SMrO5uVe48zY/NBxi/ZybXj\nFhIZUokHujdkVLu6+Hrp+RulFCSmphPqeRz821x8Z6WUUk530SmUIuIObAN6Y5uUrgCG515sLSLd\ngWXGmBQRGQ10M8bcJCKVgVggBjDASqCVMeZEQa+nUyivcJPvtT3jvAPgsd3Ft56qPDDGlvgvwnq2\nzKxsfl67j//9vpnle45R2deLRmEB+Hq54+vlgY+nO6GVKjC6UyQNwwJKIHil8qdTKC9NSRwjgx/9\njsN+9+De+aFzW70opZRymgsdHwvz7bgNsMMYE2eMSQcmAud0cDbGzDPGpDhuLsX2wgHoC8w2xhx3\nJG2zgX5FeRPqCtHrOVtQpFYHTd7yEilyMRIPdzdubFmLpf/Xlz8e7M2AxtXx9nDj1JlMdh87zcq9\nx/l40Q4av/Abt365mLijJ4s5eKVUWWSMwTvtOO5k2+nYSimlyrzCzKGqAezLdXs/0PYC+98BTL/A\nY0ug0oEqN/yC4c65F19Pp4pEROhUL5RO9ULPuy/hZBqvzN7E2D+28W3sbm5rV48n+zamdhX9t1Cq\nvDp1JpNqbo5JMQVVEFZKKVWmFOsiGBEZiZ0u2fUSH3c3cDdAzZpaKvyKV6W+syO4IoVUqsDr17fk\n4Z6NeHHmBsYt3sEnS3bQtX5VRrauzZDmNQn09br4EymlXEZSagY13BLtDR2BU0opl1CYBC4eyF0b\nPtyx7Rwi0gt4CuhqjDmT67Hd8jx2ft7HGmPGAePAzu8vRExKqRJSLcCHd29szaO9o/l8aRxfLd/F\nnd8u497vV3DNVeG0rVWFiCBfagb5ERHkR7WACri76XRXpVxRYmr62QROR+CUUsolFCaBWwFEikgd\nbEI2DBiRewcRaQF8BPQzxhzJdddM4EURCXLc7gM8cdlRK6VKXESQH//ufxVP92tC7N7jfLV8F9+t\n2sOPq/ees1+lCh68NSSG29rVReQyWxQopUpVkiOByxYP3PxCnB2OUkqpQrhoAmeMyRSR+7DJmDsw\n3hizUUTGALHGmCnAa9gK5j84vsDtNcYMMsYcF5HnsUkgwBhjzPESeSdKqRIhIrSuVYXWtarw9tBW\nJKVmsO/EafYlprDvRAoTV+7hjm+WMnvLQT4c1oYAH51mqZSrSErLoIZ7Ehm+IXhr4SillHIJhVoD\nZ4yZBkzLs+2ZXD/3Ou9BZ+8bD4wvaoBKqbJDRAj09SLQ14uratiB9Ts71OOV2Zt45rd1LNt9jIm3\ndaRN7WAnR6qUytfW6bDhh79uRh87TTPPzWT7FdCjUymlVJmjp9uUUpfF3c2NJ/s2YeEDvcnKzqbj\nG7N49rd1HDmZ5uzQlFJ5nToMB9f+dQlM2sQp401Gw2ucHZlSSqlCKtYqlEqpK1eHuiGseWIAoyeu\nYMz09bw0ayNDm0cwunMUneqF6Po4pcqCVqPsxeHDWRt5YsoaUjre5LSQlFJKXRpN4JRSxSbI15uJ\nt3fi2QFX8eGf2/liWRwTVu6hcbUARrauw+Cm4TSs6q/JnFJlRGJqOp7ublTwdHd2KEoppQpJp1Aq\npYpdo7AA3h4aQ/x/r+eTEW3x8/LgiSlriP7vVKLG/Mr//byKxXEJzg5TqSteUmoGgT6eelJFKaVc\niI7AKaVKjJ+3B3d0qM8dHeqz/0QKv27Yz+R1+3lnwVb+N3czo9rW5b0bW+PnrX+KlHKGxNR0rRyr\nlFIuRr81KaVKRXiQL6M7RzG6cxTJqRm89vsmXpi5gWV7jvL97Z1pUj3Q2SEqdcXJGYFTSinlOnQK\npVKq1Pn7ePL8wGbMurcHx0+n0/q1GXyyeAfGGGeHptQVJSlNR+CUUsrVaAKnlHKaXg2rseaJAXSs\nG8Jd3y7j6g/mMzF2NyfTMpwdmlJXhMSUDAIq6AicUkq5Ep1CqZRyqjB/H2be253X5mzmzXlbmL7p\nAN4ebvRpWI0hzWtyQ8ua+HrpnyqlSkJSWjqBvjoCp5RSrkRH4JRSTufu5sbjfRpz4IXrWPhAb+7p\nFMma+BOM+noJbV6bwc6Ek84OUalySUfglFLK9WgCp5QqM9zd3OhcP5S3hsawZ8y1TL2nGweSUmn9\n2gzmbDno7PCUKlcys7I5nZ6pI3BKKeViNIFTSpVJIsLVTWqw4pF+VA/woe/Yebw1b4sWOlFlioj0\nE5GtIrJDRB7P5/43RWSN47JNRBJz3XeriGx3XG4t3cgh2bHWVEfglFLKtWgCp5Qq0+qFVGLJw325\n5qoaPPjTSm77eiknUs44OyylEBF3YCzQH4gGhotIdO59jDEPGmOaG2OaA+8CPzseWxl4FmgLtAGe\nFZGg0ow/MTUdgABtI6CUUi5FEzilVJlXqYInP9/ZhX/3a8IXy+Ko/cxknv1tHYkp6c4OTV3Z2gA7\njDFxxph0YCIw+AL7DwcmOH7uC8w2xhw3xpwAZgP9SjTaPJJS7QhcoLYRUEopl6IJnFLKJbi5CWMG\nNmPN4wPo1SCMMdPXU/vZSfxHEznlPDWAfblu73dsO4+I1ALqAHOL8Ni7RSRWRGITEhIuO+gcOgKn\nlFKuSWtzK6VcSrPwIH66qwtr9h9nzPQNPDd9PS/M3ECrmpXpWr8qXeqH0rFuiBZmUGXNMOBHY0zW\npT7QGDMOGAcQExNTbItAdQROKaVckyZwSimX1Dy8Mj87ErkfVu1lwY4jvDlvC6/O2YQI3NauHm8P\nbUVFbx1dUCUmHojIdTvcsS0/w4B78zy2W57Hzi/G2C4qJ4HTETillHItmsAppVxa8/DKNA+vDEBq\neibLdh9j0rp9vLNgK4viEvjutk40Cy/V2hDqyrECiBSROtiEbBgwIu9OItIQCAKW5No8E3gxV+GS\nPsATJRvuuf6aQllBR+CUUsqV6Bo4pVS54ePlQbeoqrw1NIbf7+9JcmoGbV+fwfsLt2n7AVXsjDGZ\nwH3YZGwz8L0xZqOIjBGRQbl2HQZMNLk+hMaY48Dz2CRwBTDGsa3UJKXpCJxSSrkiHYFTSpVL3aPC\nWPvEAG79agn3fr+C37ceYtzwtlSp6O3s0FQ5YoyZBkzLs+2ZPLf/U8BjxwPjSyy4i0hMScfPywMP\ndz2Xq5RSrkT/aiulyq2QShWYek83Xr+uJb9uiKfJi1OZtrGgJUpKXVmS0jII9NXRN6WUcjWawCml\nyjU3N+Hhno1Y/khfgv28ufqD+dz17VJOOqaPKXWlSkpN1/VvSinlgjSBU0pdEZqHVyb20f481jua\n8UviaPrSb8zbdsjZYSnlNImpGbr+TSmlXJAmcEqpK4a3pzsvD27Bwgd64S5Cj3d+Z/BHC9h4MNHZ\noSlV6pJS07UHnFJKuSBN4JRSV5yO9UJZ9+TVvHBNM+ZvP0zTF6dx21dL2Hv8tLNDU6rU6AicUkq5\nJk3glFJXJF8vD57s24S4/wzmwe4NmbByN1FjpvCf39aRkZXt7PCUKnE6AqeUUq6pUAmciPQTka0i\nskNEHs/n/i4iskpEMkVkaJ77skRkjeMypbgCV0qp4lClojevX9+Sbc8MYkjzmjw3fT0d35jF1sPJ\nzg5NqRJjjCEpTUfglFLKFV00gRMRd2As0B+IBoaLSHSe3fYCo4Bv83mKVGNMc8dlUD73K6WU09Ws\n7Mc3ozrywx2d2ZlwkhYvT2Psgq3aAFyVS2kZWaRnZhNQQRM4pZRyNYUZgWsD7DDGxBlj0oGJwODc\nOxhjdhtj1gE670gp5dKGtqjJhqcG0jUylPt+iKX/+/OIO3rS2WEpVaySHG00An11CqVSSrmawiRw\nNYB9uW7vd2wrrAoiEisiS0Xk2vx2EJG7HfvEJiQkXMJTK6VU8asW4MO00d354KbW/LHzCI3+O5VH\nJ60iKTXd2aEpVSwSU+xnWUfglFLK9ZRGEZNaxpgYYATwlojUy7uDMWacMSbGGBMTEhJSCiEppdSF\niQj3dI5i2zODGN6qNq/N2Uzkc1P48I9tZGqRE+XidAROKaVcV2ESuHggItftcMe2QjHGxDuu44D5\nQItLiE8ppZyqRqAvn9/cnthH+9Gwqj+jv1tB9H+n8uLMDdp2QLmspFSbwOkInFJKuZ7CJHArgEgR\nqSMiXsAwoFDVJEUkSES8HT8HAx2BTUUNVimlnKVVzSoseKA3P93ZmTD/Cjz161pqPTOJ7m/PYfyS\nnZw+k+nsEJUqtETHdOAAbSOglFIu56IJnDEmE7gPmAlsBr43xmwUkTEiMghARFqLyH7gBuAjEdno\neHgjIFZE1gLzgJeNMZrAKaVckohwffOaLHywD3H/GcyYq5sSn5jCHd8spfVr09mRoMVOlGvIGYHT\nPnBKKeV6PAqzkzFmGjAtz7Zncv28Aju1Mu/jFgNXXWaMSilV5tQJrsi/+1/F0/2aMHPzQf72+SLa\nvjaDH+7oTI8GYc4OT6kLOjsCp1MolVLK1ZRGEROllCq3RIR+0dVZ/kg/wvx96DN2Lu8v3ObssJS6\noKTUdNxEqOhdqPO4SimlyhBN4JRSqhjUC6nEkof70j+6Ovd+v4LRE5dzJiPL2WEpla+k1AwCfDwR\nEWeHopRS6hJpAqeUUsXE38eTSXd34dFe0Xz453aavvQbv2895OywlDpPYmq6Tp9USikXpQmcUkoV\nI3c3N165tgUz7+1BVrah17u/M/KLRRxOTnV2aEr9JSk1QwuYKKWUi9IETimlSkCfRtVY/+TV/Ltf\nE75ftZcGz//Ku/O3kpqu7QaU8yWlZWgPOKWUclGawCmlVAnx8fJgzMBmrH/yalpFVOafP8ZS85lJ\nPDN1LYd0RE45UWJqOoG+OgKnlFKuSBM4pZQqYQ2q+jPn/p7M/1cvOtQJ4b8zN1DrmUnc/vUSth9J\ndnZ46gqUlKojcEop5ao0gVNKqVIgInSNrMrkv3dl67+v4a4O9flu1R6avTSND/7YhjHG2SGqIhCR\nfiKyVUR2iMjjBexzo4hsEpGNIvJtru1ZIrLGcZlSelHnFDHRETillHJFmsAppVQpiwz1570bW7P9\nmUF0qR/KP75bwdUfzNdplS5GRNyBsUB/IBoYLiLRefaJBJ4AOhpjGgMP5Lo71RjT3HEZVFpxZ2cb\nktMyCNQqlEop5ZI0gVNKKSepHujL9H90590bYpi3/TBNXviNX9buc3ZYqvDaADuMMXHGmHRgIjA4\nzz53AWONMScAjDFHSjnG85w6k4kx6AicUkq5KE3glFLKiUSE+7o2YNVj/alV2Y/rP17Ivd9pE3AX\nUQPInXHvd2zLLQqIEpFFIrJURPrluq+CiMQ6tl9b0IuIyN2O/WITEhIuO+jE1HQAHYFTSikXpQmc\nUkqVAY3CAljycB8e7tGI9//YTue3ZrPn+Clnh6UunwcQCXQDhgMfi0ig475axpgYYATwlojUy+8J\njDHjjDExxpiYkJCQyw4oyZHA6QicUkq5Jk3glFKqjPDycOf161vy052d2Xo4mZYvT2f6xnhnh6UK\nFg9E5Lod7tiW235gijEmwxizC9iGTegwxsQ7ruOA+UCLkg4YIDE1A0CrUCqllIvSBE4ppcqY65vX\nJPbRfoQH+TLgg/k8NWWNTqksm1YAkSJSR0S8gGFA3mqSk7Cjb4hIMHZKZZyIBImId67tHYFNpRF0\nzgic9oFTSinXpAmcUkqVQZGh/ix9uC+3t6/Hi7M20uKVafyxw+n1L1QuxphM4D5gJrAZ+N4Ys1FE\nxohITlXJmcAxEdkEzAMeMcYcAxoBsSKy1rH9ZWNM6SRwaToCp5RSrszD2QEopZTKn4+XB5/+rR1D\nm0cw+rsVdHlrNnd1qM8r1zYnyNfb2eEpwBgzDZiWZ9szuX42wEOOS+59FgNXlUaMeSWm6AicUkq5\nMh2BU0qpMq5/4xpsfGog/9ezEeOX7qTR81P56M/tpKRnOjs05YJ0BE4ppVybJnBKKeUC/Lw9eO26\nlsQ+2o/aVfy4Z+Jywp/+hccmrWbv8dPODk+5kMTUdCp4uuPt6e7sUJRSShWBJnBKKeVCmodXZsnD\nfVn4QG96Ngjj9d83U+fZyQz9ZCGbDyU5OzzlApJSM3T0TSmlXJiugVNKKRcjInSuH0rn+qHsPX6a\nD/7Yxgd/bmfK+nge7dWIp/o2wcdL/7yr/CWlZhCgTbyVUspl6QicUkq5sJqV/XhpcAu2PTOIYa1q\n8cLMjTR58Tdmbjrg7NBUGZWYmk6gNvFWSimXpQmcUkqVA6GVKvDlLR2Y+8+eeLq70e/9eQz8YB4f\n/bmdLYeSsMUQldIROKWUcnU6x0YppcqR7lFhrH18AK/O2cT7f2zjt412JK5qpQp0jQxlSPOaDGke\ngbubnr+7UiWmphMR5OvsMJRSShWRJnBKKVXOeHu68+/+V/F0vyZsP3KSBTuOsGDHYeZtO8z3q/ZS\nL7gij/aK5pa2damglQivODoCp5RSrk0TOKWUKqdEhKiq/kRV9eeujvXJys5m8rr9vDRrI3+fuJxn\np63jwe4Nub9rAy16cgVJSksnoIKugVNKKVelc2iUUuoK4e7mxvXNa7L8kX7Mub8njasF8tjkNTR/\neRpLdx11dniqFGRkZZOSnkWgr47AKaWUqypUAici/URkq4jsEJHH87m/i4isEpFMERma575bRWS7\n43JrcQWulFKqaESEng3CmHN/T2bf14O0jCw6vjGLxyatJi0jy9nhqRKUlJoOoCNwSinlwi6awImI\nOzAW6A9EA8NFJDrPbnuBUcC3eR5bGXgWaAu0AZ4VkaDLD1sppVRx6NWwGuufHMgd7evx6pxNtHxl\nGst362hceZWYmgGgI3BKKeXCCjMC1wbYYYyJM8akAxOBwbl3MMbsNsasA7LzPLYvMNsYc9wYcwKY\nDfQrhriVUkoVE38fT8aNaMuMf3QnOS2DDm/M4qWZG8jO1tYD5Y2OwCmllOsrTAJXA9iX6/Z+x7bC\nKNRjRfPIxcQAAAkfSURBVORuEYkVkdiEhIRCPrVSSqni1De6OhueHMiQ5hE8+eta+o6dy6HkVGeH\npYpRkmMETqtQKqWU6yoTRUyMMeOMMTHGmJiQkBBnh6OUUlesQF8vJt7WiXHD2/JnXALNXprGrM0H\nnR2WKiaJjhG4QB8dgVNKKVdVmLrR8UBErtvhjm2FEQ90y/PY+YV8rFJKKScQEe7qWJ/2dYIZ9tmf\n9B07l2ubhtO2djCtIirTMqIyVSp6OztMVQQ6AqeUUq6vMAncCiBSROpgE7JhwIhCPv9M4MVchUv6\nAE9ccpRKKaVKXZPqgSx/pB9P/7qWyev3M2nd/r/uq13Fjzevb8W1zSIu8AyqrNEROKWUcn0XTeCM\nMZkich82GXMHxhtjNorIGCDWGDNFRFoDvwBBwDUi8pwxprEx5riIPI9NAgHGGGOOl9B7UUopVcx8\nvTx4Y0gr3hjSihMpZ1i17wQr9x5j1b4ThPn7ODs8dYnqVKnIdc0iqFRBG7crpZSrEmPKVpWxmJgY\nExsb6+wwlFJKlQIRWWmMiXF2HK5Cj5FKKXVluNDxsUwUMVFKKaWUUkopdXGawCmllFJKKaWUi9AE\nTimllFJKKaVchCZwSimlVBGJSD8R2SoiO0Tk8QL2uVFENonIRhH5Ntf2W0Vku+Nya+lFrZRSypVp\nGSqllFKqCETEHRgL9Ab2AytEZIoxZlOufSKx7XM6GmNOiEioY3tl4FkgBjDASsdjT5T2+1BKKeVa\ndAROKaWUKpo2wA5jTJwxJh2YCAzOs89dwNicxMwYc8SxvS8w2xhz3HHfbKBfKcWtlFLKhWkCp5RS\nShVNDWBfrtv7HdtyiwKiRGSRiCwVkX6X8FgARORuEYkVkdiEhIRiCl0ppZSr0gROKaWUKjkeQCTQ\nDRgOfCwigZfyBMaYccaYGGNMTEhISAmEqJRSypWUuTVwK1euPCoie4rhqYKBo8XwPKXNVeMGjd1Z\nXDV2V40bNPbiVMvZAVyGeCAi1+1wx7bc9gPLjDEZwC4R2YZN6OKxSV3ux86/2AsW0zGyrH0GLoXG\n7hyuGrurxg0auzOUtbgLPD6KMaY0Ayk1IhJbUPfyssxV4waN3VlcNXZXjRs0dmWJiAewDeiJTchW\nACOMMRtz7dMPGG6MuVVEgoHVQHMchUuAlo5dVwGtjDHHSyFul/0MaOzO4aqxu2rcoLE7gyvFXeZG\n4JRSSilXYIzJFJH7gJmAOzDeGLNRRMYAscaYKY77+ojIJiALeMQYcwxARJ7HJn0AY0ojeVNKKeX6\nNIFTSimlisgYMw2YlmfbM7l+NsBDjkvex44Hxpd0jEoppcqX8lzEZJyzAygiV40bNHZncdXYXTVu\n0NiVa3Plz4DG7hyuGrurxg0auzO4TNzldg2cUkoppZRSSpU35XkETimllFJKKaXKFU3glFJKKaWU\nUspFlLsETkT6ichWEdkhIo87O54LEZHxInJERDbk2lZZRGaLyHbHdZAzYyyIiESIyDwR2SQiG0Xk\nX47tZTp+EakgIstFZK0j7ucc2+uIyDLH5+Y7EfFydqwFERF3EVktIlMdt10idhHZLSLrRWSNiMQ6\ntpXpz0sOEQkUkR9FZIuIbBaR9mU9dhFp4Phd51ySReSBsh63Kll6jCx5rnp8BNc/RurxsfS54vER\nXP8YWa4SOBFxB8YC/YFoYLiIRDs3qgv6HOiXZ9vjwO/GmEjgd8ftsigTeNgYEw20A+51/K7Levxn\ngB7GmGbYXkz9RKQd8ArwpjGmPnACuMOJMV7Mv4DNuW67UuzdjTHNc/VZKeuflxxvAzOMMQ2BZtjf\nf5mO3Riz1fG7bg60AlKAXyjjcauSo8fIUuOqx0dw/WOkHh9Ln8sdH6EcHCONMeXmArQHZua6/QTw\nhLPjukjMtYENuW5vBao5fq4GbHV2jIV8H5OB3q4UP+CLbZ7bFjgKeOT3OSpLFyAc+welBzAVEBeK\nfTcQnGdbmf+8AAHALhxFn1wp9lyx9gEWuVrcein2z4EeI53zHlzu+OiI0aWOkXp8dErcLn98dMTo\ncsfIcjUCB9QA9uW6vd+xzZVUNcYcdPx8CKjqzGAKQ0RqAy2AZbhA/I4pFmuAI8BsYCeQaIzJdOxS\nlj83bwGPAtmO21VwndgNMEtEVorI3Y5tZf7zAtQBEoDPHFNzPhERP1wj9hzDgAmOn10pblW89BhZ\nylzt+AgufYzU42PpKw/HR3DBY2R5S+DKFWPT/zLd50FEKgI/AQ8YY5Jz31dW4zfGZBk7ZB4OtAEa\nOjmkQhGRgcARY8xKZ8dSRJ2MMS2x07fuFZEuue8sq58XwANoCXxgjGkBnCbPlIoyHDuONR+DgB/y\n3leW41bqYsr659cVj4/gmsdIPT46jUsfH8F1j5HlLYGLByJy3Q53bHMlh0WkGoDj+oiT4ymQiHhi\nD07fGGN+dmx2mfiNMYnAPOy0ikAR8XDcVVY/Nx2BQSKyG5iInSbyNq4RO8aYeMf1Eew88za4xudl\nP7DfGLPMcftH7AHLFWIH+4VglTHmsOO2q8Stip8eI0uJqx8fweWOkXp8dA5XPz6Cix4jy1sCtwKI\ndFQd8sIOiU5xckyXagpwq+PnW7Fz58scERHgU2CzMeaNXHeV6fhFJEREAh0/+2DXJWzGHqSGOnYr\nc3EDGGOe+P/27R6loSAKw/A7lUUQ0dpCbOzEBVgIdtmBWLoMwe3YWliqCxDBH6KF2llo4wosjsVM\nMI2kSXLvCe8DA2GqLzCXj5M7iYjNiNiinu2biDgmQfZSyqCUsjr+TL1vPqLn5wUgIr6Aj1LKTts6\nBF5IkL054u9qCOTJrdmzIxcgaz9C3o60H7uxBP0IWTuy6z/hzXoBQ+CVemf7tOs8U7KeA5/AD/VX\njBPqne1r4A24Aja6zvlP9n3qa+Un4KGtYd/zA7vAfcs9As7a/jZwC7xTX6OvdJ11yvc4AC6zZG8Z\nH9t6Hj+bfT8vE/n3gLt2bi6A9QzZgQHwDaxN7PU+t2uuZ8KOnH/ulP3YsqfvSPtx4flT9mPLnrYj\nSwsrSZIkSeq5ZbtCKUmSJElLywFOkiRJkpJwgJMkSZKkJBzgJEmSJCkJBzhJkiRJSsIBTpIkSZKS\ncICTJEmSpCR+AVK5y46yy+zMAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 1080x288 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"Sg1zrX0xZTRt","colab_type":"code","outputId":"8cf01526-8dc6-45c4-f436-e88a1c2fcbcf","executionInfo":{"status":"ok","timestamp":1576424495812,"user_tz":-60,"elapsed":4170,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":137}},"source":["# Define model\n","best_model = CNN1_model(channels=64, filters=10)\n","\n","# Load best model weights from h5 file\n","best_model.load_weights(MODEL_LOCATIONS_FILE_PATH + \"/model.h5\")\n","\n","# Compile best model model\n","best_model.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics = ['accuracy'])"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"_j_iqu0tYlbS","colab_type":"code","outputId":"8335bee2-ed79-420e-8205-44bfffb48d9f","executionInfo":{"status":"ok","timestamp":1576424503761,"user_tz":-60,"elapsed":3662,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":377}},"source":["# Load topoplot coordinates\n","if not os.path.exists(CHANNEL_COORD):\n","    print(\"Missing file: {}\".format(CHANNEL_COORD))\n","else:\n","    xycoord = []\n","    # Read file content\n","    with open(CHANNEL_COORD, \"r\") as f:\n","        file_content = f.read()\n","        # Loop over all rows\n","        for row in file_content.split(\"\\n\"): \n","        # Skip missing rows\n","            if row == '':\n","                continue\n","            xycoord.append(row)\n","\n","# Create a list with x,y coordinates of each electrodes well formatted\n","coord = []\n","for x in xycoord:\n","    coord.append(x.split(','))\n","coord = np.array(coord)\n","\n","# Flip y axis in order to plot in the right way the electrodes positions\n","for i in range(len(coord)):\n","    coord[i][1] = 681 - int(coord[i][1])\n","\n","# Create a list of weights for all filters\n","nf = []\n","for filt in range(10):\n","    nf.append(abs(np.array(best_model.get_weights()[0])[0, :, filt]))\n","nf = np.array(nf, dtype=float)\n","\n","# Plot topoplot of the 10 filters in the first convolutional layer\n","fig = plt.figure(figsize=(15,6))\n","for i in range(10):\n","    ax = fig.add_subplot(2,5,i+1)\n","    ax.set_title(\"Filter #{}\".format(i+1))\n","    mne.viz.plot_topomap(nf[i], coord, cmap='Spectral_r', axes=ax, show=False)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA1MAAAFoCAYAAACymqHbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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qyHFs2vxh7n8rH26S3ds6892DixLNKvrPgdELXrF7V/CJeW/sQmoZOQ65uDHxD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Ta9edk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lRmP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\nY7pDkm7IZ6/+Kecc3MNBArxnB6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gTSyawZALbSGEbI5lQjX/bMtnMirKNZPzvJI5MY8sk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\nH3/Ax5aH7oVfIwQBy9H2zU0zUNRYdlHoCVHuraG5p4rW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rdwIuphAxRI0B+i+S5UpCAwg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cl8UNYKb80BYBHDuBpE+HV4VQFmWFgAmWdRr0eaHm9ptVq2fKOKqNvTFd0qQ+gdcazwKrL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PBFC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\n","text/plain":["<Figure size 1080x432 with 10 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"EzbVkDAwUmfa","colab_type":"code","outputId":"3ae0a45e-7222-49a3-893d-aa9395db4296","executionInfo":{"status":"ok","timestamp":1576424508945,"user_tz":-60,"elapsed":8821,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":84}},"source":["# Accuracy over the testing set\n","predictions = best_model.predict(test_features)\n","predictions = np.round(predictions)\n","\n","score = np.array(best_model.evaluate(test_features, test_labels, verbose=0, sample_weight=test_weights))\n","print(\"Model performance on test set:\\t[ Loss: {}\\tAccuracy: {} ]\".format(*score.round(4)))\n","print(\"\\nPredictions: {}\\nSolutions:   {}\".format(list(map(int, predictions))[:50], list(map(int, test_labels))[:50]))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Model performance on test set:\t[ Loss: 0.3606\tAccuracy: 0.7903 ]\n","\n","Predictions: [0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1]\n","Solutions:   [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"C_2LTwzNXOtY","colab_type":"code","outputId":"6acc408d-6c51-4cde-8f93-4a981803dd5b","executionInfo":{"status":"ok","timestamp":1576424509519,"user_tz":-60,"elapsed":9378,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":392}},"source":["# Weighted confusion matrix (noP300: 80%, P300: 20%)\n","data = confusion_matrix(y_true=test_labels, y_pred=predictions, sample_weight=test_weights)\n","\n","# Normalized confusion matrix (values in range 0-1)\n","data_norm = data/np.full(data.shape, len(test_labels))\n","\n","# Plot the confusion matrix\n","df_cm = pd.DataFrame(data_norm, columns=np.unique(test_labels), index = np.unique(test_labels))\n","df_cm.index.name = 'Actual'\n","df_cm.columns.name = 'Predicted'\n","plt.figure(figsize = (6,5))\n","sns.set(font_scale = 1.4)\n","cm = sns.heatmap(df_cm, cmap=\"Blues\", annot=True, annot_kws = {\"size\": 16}, vmin=0, vmax=1)\n","cm.axes.set_title(\"CNN1 confusion matrix\\n\", fontsize=20)\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYgAAAF3CAYAAAC/h9zqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQkRE4uVnfFCAEBHJlTIIERGJpQAhIiKxFCBERCSWAoSIiMTLz/ig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jrfWr8Uk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\nlgKEiIjE+n93KeFeEBRwvwAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x360 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"qA23jYMhGIXt","colab_type":"code","colab":{}},"source":["# Model metrics (sens, spec, ppv, npv)\n","def model_metrics(conf_matrix):\n","    tn, fp, fn, tp = list(data_norm.flatten())\n","    sens = round(tp/(tp+fn),4) # Sensitivity\n","    spec = round(tn/(tn+fp),4) # Specificity\n","    ppv = round(tp/(tp+fp),4) # Positive Predicted Value\n","    npv = round(tn/(tn+fn),4) # Negative Predicted Value\n","    return {\"Sensitivity\":sens, \"Specificity\":spec, \"PPV\":ppv, \"NPV\":npv}"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZfpCK_rFVbhR","colab_type":"code","outputId":"6bc67266-1bcb-4708-98b9-e6243d14af95","executionInfo":{"status":"ok","timestamp":1576424509522,"user_tz":-60,"elapsed":9313,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":85}},"source":["# Put model metrics into a table\n","metrics = model_metrics(data_norm)\n","\n","# Create figure\n","fig = plt.figure(figsize=(5,1))\n","ax = fig.add_subplot(111)\n","\n","# Hide graph outlines\n","for item in [fig, ax]:\n","    item.patch.set_visible(False)\n","ax.xaxis.set_visible(False)\n","ax.yaxis.set_visible(False)\n","ax.spines[\"top\"].set_visible(False)\n","ax.spines[\"bottom\"].set_visible(False)\n","ax.spines[\"left\"].set_visible(False)\n","ax.spines[\"right\"].set_visible(False)\n","\n","# Table definition\n","table = ax.table(cellText=[list(metrics.values())], \n","                     colLabels=list(metrics.keys()),\n","                     loc=\"center\",\n","                     cellLoc=\"center\",\n","                     colColours=[\"c\"]*4)\n","table.set_fontsize(16)\n","table.scale(2,2)\n","\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjwAAABECAYAAACF4e8fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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size 360x72 with 1 Axes>"]},"metadata":{"tags":[]}}]}]}